Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
Authors: Eunbyeol Cho
Jiyoun Kim
Minjae Lee
Sungjin Park
Edward Choi
Venue: NeurIPS 2025 poster ()
Keywords: Electronic Health Records (EHR)
EHR synthesis
time-series EHR
multi-table data
Primary area: machine_learning_for_sciences

Abstract: Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving as a critical resource for healthcare research and applications. However, privacy concerns and regulatory restrictions limit the sharing and utilization of such sensitive data, necessitating the generation of synthetic EHR datasets.
Unlike previous EHR synthesis methods—which typically generate medical records consisting of expert-chosen features (e.g., a few vital signs, structured codes only)—we introduce RawMed, the first framework to synthesize multi-table, time-series EHR data that closely resembles raw EHRs.
Using text-based representation and compression techniques, RawMed captures complex structures and temporal dynamics with minimal lossy preprocessing. 
We also propose a new evaluation framework for multi-table time-series synthetic EHRs, assessing distributional similarity, inter-table relationships, temporal dynamics, and privacy. Validated on two open-source EHR datasets, RawMed outperforms baseline models in fidelity and utility. The code is available at [https://github.com/eunbyeol-cho/RawMed](https://github.com/eunbyeol-cho/RawMed)

Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
Eunbyeol Cho', Jiyoun Kim', Minjae Lee", Sungjin Park', Edward Choi KAIST" FuriosaAl
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Abstract
Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving critical resource for healthcare research and applications. However, privacy concerns and regulatory restrictions limit the sharing and utilization of such sensitive data, necessitating the generation of synthetic EHR datasets. Unlike previous EHR synthesis meth- ods-which typically generate medical records consisting of expert-chosen features (e.g., few vital signs, structured codes only)-we introduce RawMed, the first framework synthesize multi-table, time-series EHR data that closely resem- bles raw EHRs. Using text-based representation and compression techniques, RawMed captures complex structures and temporal dynamics with minimal lossy preprocessing. We also propose new evaluation framework for multi-table time- series synthetic EHRs, assessing distributional similarity, inter-table relationships, temporal dynamics, and privacy. Validated two open-source EHR datasets, RawMed outperforms baseline models fidelity and utility. The code available at htttts:://// thub. womm/unnyeeo/////oooooooooooo//e /RawMed
Introduction
The digitalization of medical data has accelerated the adoption of Electronic Health Records (EHR), one of the most significant innovations in modern healtheare. EHRs systematically store various medical events, such as prescriptions and test results, along with corresponding timestamps multi-table relational database, encompassing categorical, numerical, and text data. Due to these prop- erties, EHRs serve crucial resource various medical AI studies, including clinical prediction, information retrieval, and question answering 2
However, since EHRs contain sensitive personal information, they are subject to privacy regulations that hinder data sharing and research applications [3] In response, synthetic data has emerged a promising solution, offering privacy-prest preserving alternative that can still support research and clinical applications [. Progress in generative models, especially Generative Adversarial Networks (GANs) (6). has opened up new avenues for synthesizing EHR data. Early efforts primarily generated discrete features or heterogeneous features, often overlooking the time-series aspect 7 g More recent studies incorporate both time-series and heterogeneous features, gradually approaching a closer reflection of real EHR data p [2]
Despite this progress, existing EHR synthesis studies still face the following limitations. First, there is a high dependence on feature selection. Most approaches [9]
10 12 typically select only subset of tables and columns of the entire EHR database based domain knowledge, and generate synthetic data exclusively for the features within those selected columns While this method
In this paper, a column denotes a raw data field in an EHR table, while a feature refers to a processed data representation derived from more columns. See Appendix A.I
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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simplifies the synthesis process, it limits the usefulness of the generated data. For example, if new research question or analysis requires a feature that was excluded, it impossible build predictive models or perform statistical analyses based on that synthetic EHR data. Morever, prior research suggests that models incorporating a broader range of features tend to achieve higher predictive accuracy, implying that richer synthetic data offers wider utility and more substantial value across various downstream tasks. Second, many existing approaches rely on complex, lossy preprocessing steps. Techniques such as numeric binning, term normalization, and aggregation are commonly used, but can unintentionally distort the data or obscure meaningful patterns, For instance, aggregating lab values over time may mask sudden anomalies while binning them into discrete ranges may oversimplify subtle trends, reducing the synthetic data's 's fidelity for predictive modeling.
13 14 [5]
To address these challenges, our framework, RawNed, syn- thesizes raw EHRs, multi-table time-series data that pre- serve all columns and original values in their database form, as illustrated in Figure T To implement this ap- proach, RawMed adopts text-based method, treating EHR data as text to retain original values without additional transformations (e.g., binning, aggregation, or term nor- malization). This not only reduces the risk of data distor- tion, but also makes method generalizable to various EHR data with different database schemas. However, as time-series data becomes longer, its textual representation typically grows even longer due to subword tokenization, leading to significant increase in computational complex- ity.
Table 1: Maximum number of features and time steps across datasets for each method. []]: Number of features (see Appendix Aj. [2]: Number of time steps, [3]: Uses all columns, [4]: Preserves original values.
Method
[1]
[2]
[3]
[4]
9 10
98
24
X
X
90
50
X
X
5,373
48
X
276
X
X
RawMed
333,524
243
V
V
To mitigate this, we employ Residual Quantization [6 to compress textualized time-series data, enabling autoregressive modeling in compressed latent space. This reduces sequence length and computational complexity, allowing RawMed to handle multi-table EHR datasets with numerous columns. Consequently, our framework generates raw EHRs with extensive features and time steps surpassing most existing methods (Table DD. Our key contributions are summarized follows:
1. First multi-table and time-Series EHR generation. This study introduces RawMed, the first framework for generating raw multi-table time-series EHRs, preserving all columns and original values, and demonstrating the method's 's feasibility with three primary tables.
2. Novel evaluation framework for synthetic raw EHRs. Unlike prior work that synthesizes only a subset of features, RawMed generates all elements of raw EHRs, making the quality assessment more challenging. We propose a comprehensive evaluation framework encompassing low-order statistics, downstream utility, multi-table interactions, time-series fidelity, and privacy protection.
3. Open-source validation and code release. We validated RawMed on two open-source EHR datasets, confirming its effectiveness, and plan to release all source code to support further research in synthetic EHR generation.
Related work
2.1 EHR data generation
Early research EHR synthesis primarily focused on generating single data types (e.g., diagnostic codes) using generative models such as GANs and VAFs. Subsequent studies explored mixed data
types or incorporated time-series continuity [I7 have addressed both the temporal dynamics and heterogencous nature of EHR data simultancously, a gap that has only recently started to gain attention. For example, EMR-M-GAN ( GAN-based approach for mixed-type -tpp time-series EHR data, using a Dual- VAE and Coupled Recurrent Network (CRN) to capture fnter-feature correlations and temporal dynamics. EHR- -afe [10] also adopted a GAN-based framework designed to handle real- real-world EHR complexities comprehensively. including missing data, diverse feature types, and both static and time-varying attributes. FLEXGEN- EHR [1] integrated static and time-series data, focusing on missing values via optimal transport. TIMEDIFF [2 introduced hybrid diffusion model capable of generating both continuous and discrete time-series data concurrently. To the best of our knowledge, existing studies utilize all
18
9
20
21 22
However, relatively few studies
pioneered

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Our Approach
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Figure 1: Conceptual overview of the RawMed pipeline. Left: Real-world EHR data. Center: Data generation process, Right: Resulting synthetic data. The bottom illustrates conventional approach focused feature selection and domain-speciff engineering. while the top illustrates RawMed, which minimizes domain-dependen preprocessing to generate raw EHR-like data, thereby enhancing user flexibility and utility.
columns from the original EHR database tables. Instead, they rely on a subset of features deemed important by the researchers, and heavily preprocess (e.g., numerical binning, aggregation) the original EHR data to train the generative model.
2.2 Text-based approaches for tabular data generation
With the rapid progress of NLP, studies have emerged that convert tabular data into textual form for generation purposes [23 24 This text-based approach offers several advantages: it minimizes complex preprocessing, preserves original values without information loss, and leverages the pre- trained knowledge of language models. Such benefits are particularly evident for datasets like EHRs, which often follow varying database schemas and require substantial preprocessing. Nevertheless, most existing research on tabular data synthesis focuses on single-table settings [23] 244, with comparatively few studies addressing relational database 25 and no studies synthesizing time-series relational database. Moreover, converting tabular data into text increases the length of each row due to subword tokenization, potentially inflating training and inference costs. To mitigate this, RawMed builds on text-based representations but compresses textualized data, enabling efficient autoregressive modeling a latent space. This approach reduces computational cost while preserving the fidelity of raw, multi-table time-series EHRs across diverse database schemas.
Method
RawMed is framework for synthesizing multi-table time-series EHRs, capturing complex patient trajectories with minimal lossy preprocessing. Its architecture supports heterogeneous data type and varying event counts using text-based event representation, with two modules: (1) event-level compression for compact event encoding and (2) temporal inter-event modeling for dynamic temporal relationships. This design enables precise synthesis of large-scale raw EHR datasets.
3.1 Data representation and notation
When admitted to hospital, patients typically undergo series of clinical events, such as laboratory tests and medication administrations, which are recorded as individual rows in relational tables (e.g., lab, medication). Each table includes columns such as timestamp indicating when the event occurred, item identifying specific clinical measurement (e.g., glucose) or drug name (e.g., propofol), and value (e.g., 95) and uom (unit of measure, e.g., mg/dl.) for event details. By ordering these events chronologically, patient's clinical trajectory represented time-ordered sequence.
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Formally, for patient p P. the event sequence is S = [er. .e...., .e........r where n" is the number of events. Each event ex consists of a timestamp ty. representing time elapsed since hospital admission, an event type e. (e.g., lab), and a set of column-value pairs of = {(e, uj) j = I...., m m²²) }. where Cy is the column name, by its textualized value, and m the number of columns for event type ef. Although the rimestamp is column the relational table, we isolate E. from ay to highlight its role in temporal ordering.
To create textual representation, we serialize each event by concatenating the table name c,, followed by column names and their values, excluding null-valued columns. For example, an event from the lab table with af { (item, "Glucose"), (value, "95"), (uom, "mg/dL")) is serialized as "lab item Glucose value 95 uom mg/dL". Thus, an event is denoted as ce = (t. xz.), where xy is the serialized text string. Then up is tokenized, padded or truncated a fixed length L (typically 128), and embedded to x R Given the high dimensionality of these embeddings, compression essential to model the entire patient sequence SP, a challenge also addressed in the context of text-based EHR embeddings [26].
3.2 Compressing event representations
To address this, RawMed compress the event text embedding x E RRXF where the number of tokens and F is the embedding dimension, into a discrete latent representation z E RLxxF, where L2 is the latent sequence length and F2 is the latent embedding dimension. We adopt neural network-basee compression methods, Vector Quantized Variational AutoEncoders (VQ-VAE) [2 and Residual Quantization (RQ) [16.. implemented with ID convolutional neural networks (CNNs).
Encoder and quantization The encoder Enc transforms x" into 2 Enc(x,) E RL,XF,
consisting of L- latent vectors. Each latent vector, denoted & E R Fs for simplicity, is quantized to E F. by assigning it to the nearest entry the codebook where (ut( E R F= is the embedding for index k. The quantization is defined as:
{(k: lut():) k (....,KK
vQ(2; C*) = arggiin || lut(k)]lB, kE[K]
z = lut (vq(vv C)).
Thus, the quantized latent representation z E RL. comprises L. number of z vectors. To enhance the expressiveness of the representation, Residual Quantization (RQ) decomposes each latent vector 2 R from ž into multiple quantized components. Specifically, RQ represents 2 as tuple of indices and composes it as:
RQ C, D) (ki,..., kD) E [K] z Σ lut(ka)s
where D is the quantization depth (i.e., D=1 for VQ). The RQ process initializes the residual vector as ro 2. For each depth d 1,..., it quantizes the residual obtain an index his vQ(ra-1; C)) and computes the next residual as ra = rd-1 lut (ka). Partial sums (dd) = - lut(km yield increasingly refined approximations, with z = (D))
Decoder and loss function The decoder Dec reconstructs the original embedding as x Dec Dee((z) x The model trained by minimizing combination of the reconstruction loss ||x x 11 and the commitment loss. The loss of commitment encourages Enc(x) to remain close to z used during quantization. Details on architecture and training are provided Appendices D.I and
3.3 Temporal modeling between events
To model temporal relationships between events e, we transform the patient's trajectory SP by replacing each event's 's text embedding x" with its compressed representation z!. Each z is mapped
to sequence of I.- discrete indices k. E [K]L,XD forming the compressed trajectory St = 1,..., where of is the timestamp of the i-th event.
quantized =
"The function lut refers to look-up table that maps indices to their corresponding embeddings.

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Time tokenization Each timestamp ty is tokenized into a fixed-length sequence of L1 digits, denoted 7." {0, ........99........ 9) With 10-minute resolution, t. is divided by 10 and decomposed into its tens, hundreds, and (if needed) thousands digits (e.g., 720 minutes becomes (]. This provides explicit temporal context alongside event content.
Sequence representation and autoregressive modeling Each event (T.", k.) represented as block of tokens, combining the I, time tokens from Tf with the Lı D flattened indices from ky. Concatenating these blocks yields a unified sequence:
S quantized = [7] kg. 72 Re............. Tpp k
with total length of nº X (L. L. D). This sequence integrates temporal and event-based information, enabling modeling temporal dependencies effectively. We use a Transformer-based model, TempoTransf ormer, to autoregressively predict each token based on its predecessors. The training objective minimizes the negative log-likelihood:
1sp
quaited
CAR
Σ
log P(s s
pEP
where s denotes the i-th token in SP given all preceding tokens.
quamtized: and (( 1.)
is the probability of predicting token sf
3.4 Data sampling and postprocessing
Synthetic patient trajectories are generated by autoregressively sampling token sequences using the trained T ormer with Top-k sampling. Each synthetic sequence ŠP interleaves time tokens of and event tokens A," Sampling is constrained to ensure structural integrity: the first Le tokens per block, representing time, are sampled from the time token vocabulary [0, 9j. while the subsequent L. D event tokens sampled from the event token vocabulary [K]. with invalid token probabilities masked. Next, the event tokens are mapped to latent representations z! via codebook C, decoded by Dec into text embeddings xL, and converted to text (e.g., lab item Glucose value 95 uom mg/dL). Time tokens are detokenized into timestamps (e.g., [7, 2] - 720 minutes). These text-based events and timestamps are then converted to relational tables.
Transf
Although the generated data typically reflect the original table and column names, we observed rare but noticeable issues such as misspelled table or column names or extraneous characters in numeric fields. We hypothesize these errors result from both the quantization step and the autoregressive token prediction step. To ensure accurate table construction, we apply the following postprocessing steps: (1) Event-level verification: Each event e, is validated to ensure that it begins with valid table name and adheres to the column- value pair format. Misspelled column names corrected by matching to the closest valid name using Levenshtein distance, and extraneous characters are removed from numeric fields. Only events that pass all checks after corrections retained for further processing. (2) Patient-level validation: Subsequently, any sequence S containing any invalid events of are discarded. Retained sequences any timestamps that do not follow chronological order or fall outside the observation window. Valid sequences then converted into relational tables, followed by further postprocessing column-specifie constraints (e.g., numeric ranges). These steps collectively ensure that the final tables structurally and semantically consistent with the expected format. Further details are provided in Appendix D.2 and the postprocessing algorithm is outlined in Algorithm I
checked for temporal consistency, removing events starting from
enforce
4
Experiments
4.1 Setup
Dataset In this study, we used two publicly available EHR datasets, MIMIC-IV 28 and eICU 29 Adhering to the GenHPF [15] framework, which utilizes all EHR features for diverse prediction tasks, we focused on patients aged 18 years older who had been admitted to the ICU for more than 12 hours. Based on this framework, we integrated three time-series tables (laboratory tests, prescriptions, and input events) from both datasets. To assess the generalizability of our approach
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across diverse clinical settings, we conducted additional experiments on the MIMIC-IV-ED [30
emergency department dataset, with results reported in Appendix F3 After preprocessing. the datasets comprised 02,704 patients (about 7 million rows) from MIMIC-IV and 143,812 (about 7 million rows) from eICU, with summary statistics detailed in Table 7 The dataset was split into a 9:1 ratio, allocating 90% for training and 10% for testing. The generative model was trained exclusively with the training data. We aimed to generate data across all columns to preserve the original dataset as comprehensively possible; however, we excluded columns offering no analytical value (e.g.. subject_id, id, orderid). Details on exclusions are provided in Appendix B.3
This study is the first to synthesize raw EHR data, and therefore does not share an identical problem formulation with prior EHR synthesis research, making direct comparisons challenging. Because existing methods rely on predetermined feature selection and complex preprocessing steps, it is infeasible to extend them to the broader generative scope pursued in this work (i.e., all elements in the original database structure). To provide a baseline, we adapt RealTabFormnr [25, designed for text-based relational databases rather than time-series relational databases. Built on GPT-2, RealTabFFrrmer is constrained by limited context length, restricting the amount of data it can process. To address this, we concatenate time-series data in chronological order and apply QLoRA A [31] to Llama 3. [32] rather than GPT-2 [33), allowing for longer sequences. Moreover, RealTabFormer does not compress data but instead models it directly, effectively serving as no-compression baseline.
Baseline
Additionally, we include baselines using SDV [4] (Gaussian Copula-based), RC-TGAN [35 (GAN- based), and ClavaDDPM (66 (diffusion-based for multi-table generation. These methods, while not designed for time-series multi-tabe taate data, can generate synthetic data by modeling timestamps as standard columns within each table, as is typical in EHR database structures. However, these approaches might struggle to capture the temporal dynamics due to their lack of explicit temporal modeling. Appendix E.1 details the implementation and technical specifications.
4.2 Evaluation framework for synthetic multi-table time-Series EHRs
4.2.1 Existing evaluation frameworks
Evaluation of synthetic multi-table time-series data, particularly for raw EHRs, remains underex- plored. Existing frameworks, such as SDMetrics and Synthcity [37], primarily focus on single-table evaluation. In multi-table scenarios, these frameworks typically average metrics across individual tables or merge tables into a single one, obscuring inter-table relationships and temporal dynamics critical to EHR data. To our knowledge, no framework evaluates all components of raw multi-table EHR data while preserving its time-series structure. To address this, we propose an evaluation framework for RawMed that integrates standard metries (e.g., CDE), adapts metrics to handle the heterogeneity of raw EHRs (e.g., I-CDE), and introduces novel temporal fidelity metrics (e.g., Time Gap and Next Event Prediction). Detailed metric formulations are provided in the Appendix C
4.2.2 Single-table evaluation
Single-table evaluation assesses the fidelity of synthetic data within individual tables, treating each row as an instance. To capture distributional similarities, we employ both low-order statistics and high-order metrics.
Density Estimation (CDE) evaluates the similarity of marginal distributions for each column. For numeric columns, we use the Kolmogorov-Sminog (KS) statistic (range: [0.1]), and for categorical columns, we use the Jensen-Shannon (JS) distance (range: [0.1]), both with lower values indicating higher similarity. In EHR data, a single column (e.g., amount column) often contains measurements for diverse items (e.g., about 2,000 drugs), identified by the item column (e.g., itemid or drug). To address this, Item-specific Column-wise Density Estimation (I-CDE) filters data for specific items (e.g., creatinine), computes CDE per item, and averages the results across items to ensure the synthetic data preserves the unique characteristies of each clinical entity for precise leee-level fidelity.
Column-wise
Pairwise Column Correlation (PCC) and Item-specific Pairwise Column Correlation (I-PCC) assess inter-column dependencies in synthetic data by comparing correlation matrices of real and
atttps://ooss weddv.de//sdeetricc

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Table 2: Results of single-table evaluation MIMIC-IV and eICU datasets. Metrics are averaged across columns and tables for each dataset. Lower values are better, with best in bold.
MIMIC-IV
etCU
Column-wise
Pair-wise
High-order !
Column-wise J
Pair-wise
High-order
Model
CDE
I-CDE
PCC
I-PCC
ER
SMAPE
CDE
I-CDE
PCC
I-PCC
ER
SMAPE
Real
15.35
60.85
44.05
43.42
SDV
0.11
0.54
0.26
103.85
0.13
0.61
0.19
0.19
101.97
RC-TGAN
0.26
0.54
0.18
0.28
38.21
97.26
0.34
0.59
0.18
0.21
69.08
111.25
ClavaDDPM
0.06
0.22
27.91
80.02
0.06
0.26
0.07
0.19
RazNed
0.04
0.05
0.04
0.10
19.69
57.31
0.05
0.08
0.06
0.10
45.58
48.42
synthetic tables. For PCC, matrices are computed using Pearson's 's correlation coefficient (range: [)), for numeric-numeric pairs, Theil's U statistic (range: [0. 1J) for categorical-categoricc pairs, and correlation ratio (range: [0, IJ) for categorical -ummeri pairs. The mean absolute difference (Haab) quantifies dependency fidelity by averaging clement-wise matrix differences. I-PCC extends this by filtering data for each item, computing Mabs per item's correlation matrix, and averaging across items for precise item-level fidelity.
For high-order dependencies, Predictive Similarity evaluates the ability of synthetic data to model complex, non-linear dependencies in single-table data through predictive performance. An XGBoost model is trained with each column as the target and remaining columns as inputs, with performance evaluated on real test data. For numeric targets, we use Symmetric Mean Absolute Percentage Error (SMAPE; range: [0,200]); for categorical targets, we use classification error rate (ER; range: [0,100]). A smaller performance gap between models trained on synthetic data compared to those trained on real data indicates effective capture of high-order dependencies.
4.2.3 Time-series multi-table evaluation
EHR data are typically stored across multiple tables, each containing time-series events for individual patients. To address the complex interactions among these tables, preserve both the original structure and time-series aspects. Each patient identified by a primary key (e.g., stag._tta__ae stay_tdd treated as
an instance.
We evaluate the Clinical Utility of synthetic data for downstream predictive tasks a multi-table EHR setting. Unlike single-table data, multi-table EHR data exhibit significant variability in event types and lengths per patient, necessitating comprehensive representations. To address this variability, we employ two methods: GenHPF [15,, which lists all events sequentially as single textual sequence, and MEDS- BAD (38), which aggregates events into fixed time intervals (see Appendix E.2. for details). For evaluation,
define 11 clinical prediction tasks (see Table 10), and report the Area Under the Receiver Operating Characteristic Curve (AUROC) for models trained on synthetic data compared to those trained on real data, using real test data.
We also perform Membership Inference Attacks (MIA)
39 to evaluate privacy leakage in syn- thetic data by measuring distances between synthetic samples and the training/test dataset to infer membership, where successful identification indicates inadequate privacy protection.
To evaluate the temporal fidelity, we introduce two metrics: Time Gap and Event Count. Time Gap quantifies the similarity of distributions of time gaps between consecutive events for each patient, employing the KS statistic (range: [0,1]) on timestamps. Event Count assesses the distribution of event counts per patient, also using the KS statistic, to evaluate whether synthetic data accurately reflects the frequency of clinical events.
Finally, Next Event Prediction evaluates temporal sequence dynamics using an LSTM 40]-based model to predict the next event's item or drug name, formulated as a multi-label classification task for concurrent events. The model is trained separately synthetic and real data, with performance evaluated on real test data using the Fl score, quantifying the synthetic data's 's ability to capture event sequence patterns.
Results
In single-table evaluations, as presented in Table RawMed demonstrates superior performance across CDE and PCC metrics. Notably, ClavaDDPM exhibits performance closer to RauMed compared to
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Table Clinical utility evaluation on MIMIC-IV and eICU datasets for downstream clinical tasks. Performance is measured by micro- ahicro-average AUROC across 11 clinical prediction tasks (see Table [3 for individual task performance) under MEDS-TAS and GenHPF representations.
MIMIC-IV
eCCU
Model
MEDS-TAB
GenHIP
MEDS-TAB
GenHIF
Real
0.90=0.06
0.82+0.09
0.87=0.08
0.80=0.09
SDV
0.46110.1
0.471
0.46.10.10
0,4810.08
RC-TGAN
0.111
0.5110.11
0.4710.13
0.4810.11
CIavaDDPM
0.68=0.19
0.64+0.17
0.66+0.11
0.63=0.12
RawMed
0.87+00.88
0.80=0.09
0.83+0.10
0.78=0.10
Table 4: Temporal fidelity and privacy evaluation MIMIC-IV and eICU datasets. Next Event Prediction FI scores are averaged over three random seed runs with std. The best results are in bold.
Next Event Predict (F1) f
Time Gap
Event Count +
MIA (Accuracy)
Model
MIMIC-IV
eICU
MIMIC-IV eICU MIMIC-IV eICU
MIMIC-IV eICU
Real
0.18+0006
0.30++0.0
SDV
0.0510.01
0.13100.00
0.76
0.50
0.46
0.36
0.499
0.502
RC-TGAN
0.02.1000
0.07 10.01
0.43
0.39
0.04
0.03
0.500
0.500
ClavaDDPM
0.06:20.00
0.12+:0.00
0,48
0.41
0.11
0.05
0,500
0.499
RawMed
0.16#0.00
0.25=0.00
0.01
0.03
0.02
0.05
0.498
0.497
SDV and RC-TGAN. However, item-specific metrics, I-CDE and I-PCC, RawMed significantly outperforms ClavaDDPM. This underscores RawMed's capability accurately preserve the character- istics of individual clinical items. Additionally, in Predictive Similarity, RawMed achieves the lowest SMAPE and ER among baselines, closely matching real data, demonstrating semantic consistency with the original dataset alongside robust I-CDE and I-PCC performance. For a detailed results on table-wise and column-wise fidelity, refer to Table 11 and 12
In multi-table time-series evaluations, the performance disparity between RawMed and baseline methods becomes more pronounced. For Clinical Utility, Table shows RawMed's AUROC scores closely match real data downstream prediction tasks, surpassing ClavaDDPM, SDV, and RC- TGAN in both representations. Regarding temporal metrics, as shown in Table 4 RawMed excels in replicating inter-event interval distributions (Time Gap), achieving outstanding scores of 0.01-0.00 against baselines' 0.41-0.76. For Event Count, RawMed records competitive scores of 0.02 (MIMIC- IV) and 0.05 (cICU), though RC-TGAN outperforms it in cICU (0.03). RawMed also excels in Next Event Prediction, capturing event sequence patterns more effectively than other methods. Additionally, RawMed exhibits minimal vulnerability to membership inference attacks (MIA), performing near random guessing levels, thus ensuring robust privacy preservation.
Collectively, these results highlight RawMed's s efficacy in generating multi-table time-series EHRs data that closely mirrors real data in terms of temporal precision, clinical utility, and privacy preservation. In contrast, baselines such as SDV, RC-TGAN, and ClavaDDPM underperform due to their lack of explicit temporal modeling and limited capability to model complex, large-scale EHR data.
Compressed vs. Non-compressed approaches We compared RealTabFormer (RTF, non- compressed) and RawMed (compressed) to evaluate the effect of sequence compression synthetic data quality R RTF models data in the text space, handling maximum sequence lengths 11.2k for MIMIC-IV and 3.1k for eICU. In contrast, RawMed operates in the latent space, reducing these to 1.8k (84% reduction) and 0.8k (74% reduction), respectively. As reported in Table [5] RawMed outperformed RTF in most metrics, including time gap metrics of 0.05 (MIMIC-IV) and 0.01 (cICU) compared to RTF's 07 and 0313, respectively. Thus, RawMed demonstrates superior data fidelity and temporal accuracy while significantly reducing sequence length.
'Experiments typically used a 12-hour observation window for training and synthesis, but both RTF and RauMed used a 6-hour window in this experiment due to RTF's context-size limits. We synthesized about 20k samples match RTF- generaeedt sample counts, generating the original dataset size was infeasible for RTF.
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Table 5: Results on MIMIC-IV and eICU datasets with a 6-hour observation window, comparing RealTabFormer (RTF) and RawMed. Lower is better, best in bold.
Table Ablation study on MIMIC-IV dataset. Variants exclude RQ (Residual Quantization), Time Tok. (Time Tokenization), and Time Sep. (Time Separation). Lower values are better, best in bold.
MIMIC-IV
eICU
Variants
CDE
PCC
Time Gap
# Events
Metric
RTF
RawMed
RTF
RawMed
Full Method
0.04
0.04
0.01
0.02
CDE +
0.11
0.04
0.21
0.06
PCC
0.08
0.03
0.19
w/o RQ
0.05
0.04
0.05
0.13
Time Gap
0.17
0.05
0.13
0.01
Time Tok.
0.04
0.04
0.04
0.12
w/o Time Scp.
0.07
0.07
0.51
0.40
# Events L
0.20
0.07
0.07
0.08
(a) Real
(b) VQ Recon
VQ (4Comp.) VQ (2x Code) (e) RQ Recon
(f) RQ Synthetic
Figure 2: Comparison of VQ- VAE and VAE for the patientweight column in MIMIC-IV. Sub-
figures: (a) real data, (b) VQ reconstruction, (c) VQ with less compression, (d) VQ with doubled
codebook, (e) RQ reconstruction, (f) RQ synthetic data. Shared x-axes with proportional y-axes.
Ablation Studies
Why RQ is more suitable Although Vector Quantization (VQ) offers robust compres- sion within the RasMed framework, it does not consistently achieve high-fidelity representation across all data types. In text-based EHR data, columns with low correlation and independent distributions (e.g., ) are challenging to encode with VQ's limited codehook, resulting in significant distortion of the original distribution upon reconstruction, compression ratio (Figure or doubling the codebook size (Figure d)d) that these data reconstructed, not generated- to fully resolve distortion these numerical columns. In contrast, RauMed employs Residual Quantization (RQ) for multi-stage quantization, effectively pre- serving independent column distributions (Figure e) (f)- Table 14 shows that for patientweight in MIMIC-IV, VQ's KS statistic (0.28) far exceeds RQ's 's (0.09), confirming VQ's greater distortion.
RQ VQ:
shown in Figure 2b). Halving the
Ablation study of RawMed components Table 6 evaluates RawMed variants with components removed, with lower metric values indicating better performance. The full method achieves optimal results across all metrics. Removing RQ (i.e., using VQ) degrades performance, increasing CDE to 0,05, Time Gap to 0.05, and Event Count to 0.13. Excluding Time Tokenization, which decomposes timestamps into digits with a 0-9 vocabulary, impairs time pattern recognition, raising Time Gap to 0.04 and Event Count to 0.12. Omitting Time Separation, which isolates timestamps for explicit temporal modeling, causes the largest performance drop, elevating CDE to 0.07, Time Gap 0.51, and Event Count to 0.40. These results highlight the critical role of each component in RawMed's temporal modeling efficacy.
Scalability In this study, we conducted experiments primarily with a 12-hour observation window. also testing 6-hour and 24-hour windows evaluate RawMed's scalability. As shown in Table [5] most metrics remained stable across windows, though the KS statistics for the event count increased slightly for the 24-hour window. This demonstrates RawMed's potential to generalize across different time scales.
7 Discussion
This study introduces RawMed, a framework for synthesizing multi-table time-series EHR data while retaining all columns and original values. By enabling hospitals to generate high-fidelity synthetic EHRs with minimal preprocessing and providing users with realistic data for flexible downstream tasks, RawMed can accelerate medical AI research while safeguarding patient privacy. However,

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several limitations remain with respect to fully encompassing the complexity of real-world medical settings. First, this work focuses on three primary tables, which leaves the question whether it can handle significantly higher number (e.g., dozens) of tables. In addition, extended time-series data might require advanced compression or sampling methods. While the framework could potentially incorporate conditional generation (including static attributes such as gender or birth year), this study, as an initial approach, focuses on unconditional generation, leaving these specialized applications for future research. In subsequent work, we plan to expand the range of tables, implement conditional generation, and more fully integrate static features into the model design, thereby providing more realistic synthetic EHR data for broader array of medical Al tasks.
Acknowledgments and Disclosure of Funding
This work supported by the Institute of Information & Communications Technology Planning & Evaluation (ITP)) grants (No.RS-2019- R---000---119000000000000 1190555, the Korea Health Industry Development Institute
(KHIDI) grant NooHHR1C0019, No.RS- 20S-2025-0021135500 and the National Research Foundation of Korea (NRF) grants (NRF-20020HH1D3324001000009 funded by the Korea government (MSIT, MOHW).
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Question:
Answer: [Yes]
Justification: The paper uses Meta-L.laMA-311-88 for Real Fabrrmer,,,, described Ap- pendix as baseline method.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https://neurd) autto:://eaarrr...c///oooffrrrer hctp////ooeeeeeecce//0/////... for what should or should not be described.
19

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A Feature definitions in EHR data
A.1 Feature definition
In electronic health record (EHR) data, tables (e.g., lab, medication, infusion) contain columns, some of which item columns (e.g.. itemid, drug, lahname) that identify clinical events, such which lab tests or medication administration. A feature a processed representation data derived from one more columns, typically created by selecting, combining, transforming column data, as described in the Introduction."
To define feature from EHR data, the following steps are typically applied:
Select specific item: A specific item is selected to represent a clinical event. For example, in a laboratory table, selecting itemid = 12345 might isolate all instances of a "blood glucose" test.
Incorporate relevant categorical columns: Related categorical columns (e.g., dose josse_uit, or route) further refine the feature definition. For instance, two rows with the same itmiid but different uom values, such as "mg/dL" vs. "mmol/L", would represent distinct features.
Numerical columns (e.g,, valuenum, amount, or rate) provide the actual measurement or dosage values for the item. Typically, a feature can be defined by combining item, a categorical column (e.g.. uom), and numerical column's 's values. Each numerical column may also represent a distinct feature when it captures a unique aspect of clinical event, such as dosage rate versus total amount.
Include numerical columns:
For example, "blood glucose level" defined by valuenum where itemid = 12345 and uom = "mg/dL". While feature engineering is commonly used, this section focuses the core structure of features, excluding feature engineering techniques (e.g., aggregation or transformation).
A.2 Feature count calculation
To calculate the number of features in EHR data, we define two approaches: the theoretical maximum (Possible Feature Count) and the practical estimate (Actual Feature Count). The data consists of a set of tables E, such as medication, laboratory, or infusion tables. Each table E & includes categorical columns Cr and numerical columns Ce.mum-
The following definitions are essential for calculating feature counts:
Distinct Items (I,): The set of unique items in table e, representing distinct clinical event identifiers an item column. For example, in a medication table, each item E I corresponds to drug name (e.g., paracetamol, aspirin).
Number of Unique Values in Categorical Column (VVi,o)): The count of unique values in categorical column E Cr, (e.g., administration route) for item For instance, if paracetamol has administration routes "intravenous" and "oral," then [V = 2.
Number of Unique Categorical Value Combinations ((Cenin (i)):: The number of ob- served combinations of all categorical columns values for item i. For example, if paracetamol has three combinations of route and dose unit, then Cemiique (i) = 3.
Number of Numerical Columns (/Ce.numl) ) )): The count of numerical columns (e.g., amount, rate) associated with each item-category combination.
Possible Feature Count The Possible Feature Count assumes all possible combinations of categor- ical values exist for each item, providing theoretical upper bound. It is calculated
Σ II (Viel) (CCe.uml + 1)
Possible Feature Count
<EE
iEI.
eEC..on
where IoEE, ce |Viel represents the number of possible categorical combinations, and |Ce.umml + accounts for the numerical columns plus a indicator feature representing the presence or absence of the categorical combination.
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Actual Feature Count The Actual Feature Count considers only the categorical combinations observed in the data, reflecting dependencies or sparsity that reduce the number of combinations. is calculated as:
Actual Feature Count =
Σ FEE
Σ VEL.
Cunique (i)
x (|Ce,numl + 11
where Cunique (i) is the number of actual combinations, and |Ce,numl
accounts for numerical
columns and the presence/absence feature.
For example, consider a medication table with 100 unique medications (drug), two categorical columns (dose_unit with 5 unique values and roule with 3 unique values), and numerical columns (amount and rate). If all 5 x 3 = 15 combinations of dose_ umit and route are valid, the features per medication are calculated as 15 (2 + 1) 45, resulting in a total of 100 45 4, 500 features. However, if only 10 combinations of dose wnit and route are present, the features per medication decrease 10 X (2 + 1) 30, and the total feature count reduces to 100 30 3, 000.
In summary, the Possible Feature Count provides a theoretical maximum, while the Actual Feature Count reflects the data's sparsity and structure for realistic estimate. This study used the Actual Feature Count to calculate the number of generated features [T)
B Dataset statistics
B.1 Dataset selection criteria
To synthesize time-series electronic health records (EHR), we selected ICU patient datasets, specifi- cally MIMIC-IV and eICU, for their high temporal resolution and continuous monitoring. MIMIC-IV, collected from Beth Israel Deaconess Medical Center, includes 94,458 ICU stays across 364,627 patients, while eICU, multi-center dataset, comprises 200,859 ICU stays from 139,367 patients across U.S. hospitals. These datasets provide large cohorts and diverse clinical variables, making them established benchmarks for validating most prior EHR synthesis studies. We focused on time-series tables, labevents, prescriptions, and inputevents from MIMIC-IV, and lab, medication, and infusiondrug from eICU. These tables capture critical temporal changes physiological states, treatment interventions, and drug administration. enabling the synthesis of complex clinical patterns. In contrast, emergency department (ED) and outpatient datasets, with irregular intervals and lower resolution, are typically less suitable for synthesizing the continuous temporal dynamics of patient trajectory.
However, to evaluate the model's generalizability, we additionally utilized the MIMIC-IV-ED dataset validate its ability to synthesize data in distinct clinical setting. From MIMIC-IV-ED, we synthesized the vitalsign (time-series data such as heart rate and blood pressure), medrecon (static data medications at admission), and pyxis (event-based data medication dispensing via the Pyxis system) tables. By simultancously generating time-series (vitalsign, pyxis) and non-time-serier (medrecon) data, the model effectively synthesized complex patterns ED data, demonstrating robustness across diverse clinical scenarios.
B.2 Data statistics
This study utilized the publicly available MIMIC-IV and eICU datasets, focusing on patients aged 18 years or older with ICU stays exceeding 12 hours. Key metrics, including patient counts, event frequencies, and row counts for the tables, summarized in Table Additionlll, ablation studies exploring 6- and 24-hour observation windows for MIMIC-IV and eICU, and 6-hour window for the MIMIC-IV-ED dataset in ED settings, are detailed in Table
B.3 Criteria for excluded columns
In this study, we aimed to utilize as many columns as possible but excluded specific columns for the following reasons and generated all others. First, we removed patient or prescription identifiers and unique hospital-specific codes (e.g., subject id, orderid, pharmacy that were not directly relevant to the clinical information used by the model.
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Table 7: Summary statisties for preprocessed MIMIC-IV and eICU datasets with 12-hour ob- servation window, including patient counts, event frequencies, and row and column counts for lab (labevents lab), medication (prescriptions or medication), and input (inputevents or infusiondrug)
data. Note: M denotes million.
Metrie
MIMIC-IV
eCCU
Number Patients
62,704
143,812
Max. Events per Patient
243
114
Avg. Events per Patient
108.8
47.2
Total Rows
6.8M
6.8M
Rows: Lab
3.8M
4.6M
Rows: Medication
1.7M
1.6M
Rows: Input
1.4M
0.6M
Columns: Lab
6
Columns: Medication
6
Columns: Input
6
Table Summary statistics for preprocessed MIMIC-IV-ED (6-hour observation window), MIMIC-
IV, and eICU (6- and 24-hour observation windows) datasets. Note: M denotes million.
Metric
MIMIC-IV-ED MIMIC-IV (6h) MIMIC-IV (24h)
eCCU (6h)
eICU (24h)
Observation Window (hrs)
24
Number of Patients
144,790
63,903
51,357
132,202
119,641
Max. Events per Patient
34
165
366
79
179
Avg. Events per Patient
16.5
71.6
170.2
32.0
74.1
Total Rows
2.4M
4.6M
8.7M
4.2M
8.9M
Second, excluded columns that contained multiple timestamps or offset columns conveying similar time information (e-g., charttime, starttime). While each timestamp could potentially have clinical value, we consolidated them into single representative time field reduce analytical complexity.
Finally, we excluded columns that potentially contained future information beyond the event time to avoid overestimating model performance by training on data not available the actual prediction point. All remaining columns were included in the modeling process. Table summarizes all excluded columns and indicates the primary reason for each exclusion.
C Evaluation framework details
To rigorously evaluate the quality of synthetic multi-EHR data generated by RawMed, we define comprehensive set of metrics that assess both single-table fidelity and multi-table temporal dynamics. Each metric is carefully selected to capture specific aspects of data quality, such as distributional similarity, inter-column dependencies, temporal fidelity, clinical utility, and privacy. Below, provide detailed definitions, mathematical formulations, and justifications for each metric, supported by visualizations where applicable. Our evaluation pipeline is designed to be robust and tailored to the unique challenges of raw multi-table time-series EHR data, providing a thorough assessment of synthetic data quality.
C.1 Single-table evaluation
Single-table evaluation focuses on fidelity synthetic data within individual tables, treating each row an independent instance. We combine low-order and high-order metries capture marginal distributions and complex inter-column dependencies. Below, we detail each metric, formulation, and its relevance to EHR data evaluation.
Column-wise Density Estimation (CDE): CDE measures the similarity of distributions between real and synthetic data for each column. For numeric columns, use the Kolmogorov-Sminng Smimnov (KS) statistic, defined as:
KS sup F+(x) Fs(a),
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Table 9: Columns excluded from each dataset table, categorized by the reason for exclusion: identifiers or codes, time-related columns, or columns containing potential future information.
Dataset
Table
Excluded Columns
Reason: Identifiers or Codes
MIMIC-IV labevents
labevent, id, subject_in id, specimen id, order sndder_rovider_tte id subject_ id, caregiver_ id
MIMIC-IV inputevents
MIMIC-IV preseriptions
subject_id, id, pharmacy_ id, poe_id, poe_seq, order_ wrder_providerjid orderid, linkorderid labid
eICU lab
eICU eCCU
infusiondrug medication
infusiondrugid
medicationid, drughiclseqno, gte
Reason: Time Columns
MIMIC-IV
labevents inputevents
storetime
MIMIC-IV
endtime, storetime
MIMIC-IV
prescriptions lab
stoptime
eICU
labresultrevisecdoffst
eICU
medication
drugorderoffset, drugstopoffset
Reason: Future Information
Leakage
MIMIC-IV prescriptions
statusdescription
eCCU
medication
drugordercancelled
where Fr (z) and Fx(x) are the cumulative distribution functions (CDFs) of the real and synthetic data, respectively. The KS statistic ranges from [0, 1]. with lower values indicating higher similarity. For categorical columns, we use the Lensen-Shanme (JS) divergence, defined as:
Is( = KL(P.IIM 1 +
KL(P.I|M).
where P. and P. are the probability distributions of the real and synthetic data, M $ (P, + P.). and KL is the Kullback-Leiler divergence. JS ranges from [0, 1], with lower values indicating greater similarity.
Justification: CDE is a fundamental metric for comparing distributional similarity across tabular data. It effectively captures the distributional properties of numerical columns (e.g., laboratory values) and categorical columns (e.g., prescribed drug names) in EHR data. This ensures robust evaluation across diverse column types, critical for synthetic data validity.
Item-specific Column-wise Density Estimation (I-CDE): I-CDE extends CDE by evaluat- ing distributional fidelity for specific clinical items (c.g., creatinine, glucose) within columns that aggregate multiple item types (c.g.. value column linked to itemid). For each item, we filter the data and apply the same KS or JS metrics as CDE. To ensure robustness, we exclude items with fewer than 0.1% of total records (e.g., lab tests occurring 1-2 times in 3.8 million events) to avoid skewed results due to extremely rare events.
Justification:
I-CDE addresses the heterogeneity of EHR data, where single column may represent diverse clinical entities (e.g., 2,000 drugs). By ensuring synthetic data preserves item-specific distributions, I-CDE enhances clinical validity.
Pairwise Column Correlation (PCC): PCC assesses the fidelity of inter-column depen- dencies by comparing correlation matrices of real and synthetic data. For numeric-numert pairs, we Pearson's correlation coefficient (p [-1, 1]). For categorical-categon pairs, we use Theil's U statistic (U E [0,1)). which measures conditional entropy. For cntegorical-numert pairs, use the correlation ratio (n) E [0, 1)), which quantifies the variance explained by the categorical variable. The mean absolute difference (Habs) between real and synthetic correlation matrices is computed as:
Habs
Σ
Corr, Corr,(i,j)l.
N
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---

where Corr, and Corr, are the correlation matrices for real and synthetic data, and N is the number of matrix elements.
Justification: PCC captures dependencies between clinical variables ge. heart rate and blood pressure), essential for realistic synthetic EHR data. The use of multiple correlation measures ensures applicability to mixed data types in EHRs.
Item-specific Pairwise Column Correlation (I-PCC): I-PCC extends PCC by computing correlation matrices for each item-specific subset of the data (e.g., all records for specific itemid). The jabs is calculated for each item's matrix and averaged across items. Items with fewer than 100 records excluded.
Justification: I-PCC evaluates item-specific inter-column dependencies, ensuring that synthetic data captures relationships within subsets of EHR data (e.g., correlations between dosage and frequency for a specific drug). This is critical for maintaining clinical relevance.
Predictive Similarity: Predictive Similarity evaluates the ability of synthetic data to capture high-order, non-linear dependencies by training an XGBoost model to predict each column using the remaining columns as features. Models are trained on synthetic data and evaluated on real test data. For numeric targets, we use Symmetric Mean Absolute Percentage Error (SMAPE), defined as:
100
1
D1
SMAPE
1 1022)/2
where Y4 and Os are the real and predicted values, and SMAPE ranges from [0.200]. For categorical targets, we use the classification error rate (ER), defined as:
1
ER
T( + 5z) 100,
where ER ranges from [0,]. A smaller performance gap between models trained on synthetic versus real data indicates better capture of dependencies.
Justification: Predictive similarity captures complex relationships that low-order metrics may not detect, indirectly assessing semantic consistency. While Mean Squared Error (MSE) and Mean Absolute Error (MAE) are commonly used regression metries, their ocale-dependency led to the adoption of SMAPE, which is bounded between 0 and 200 for consistent evaluation across varying ranges.
C.2 Time-series multi-table evaluation
Multi-table EHR data involve time-series events across multiple tables, linked by a primary key (e.g.. stay_id) Our evaluation metries preserve this structure and assess temporal fidelity, clinical utility, and privacy. Below, we detail each metric and its rationale.
To assess clinical utility, we evaluate the performance of synthetic data downstream predictive tasks. We defined 11 clinical prediction tasks (e.g., predicting creatinine and hemoglobin levels), following the task definitions from [1]]. Details are provided in Table IO To address variability in the number and types of events across patients, we employ two representation methods, GenHPF and MEDS- TABB, as detailed in E.2.1 For each task, the synthetic data is split into length T (e.g., T = 12 hours. The first half (e.g., the first 6 hours) serves as input for the predictive model, with predictions made at the midpoint (2) For multi-class tasks, the label is the last value of the lab measurement in the second half (e.g., the subsequent 6 hours). For binary tasks, the label indicates whether a medication event occurs in the second half. Models are trained on synthetic data and tested on real data, with performance measured using the Area Under the Receiver Operating Characteristic Curve (AUROC). Higher AUROC scores indicate better utility for clinical applications.
Clinical Utility:
segments over observation window
Justification: Clinical utility reflects the practical value of synthetic data for real woal-world EHR applications. Using two representation methods ensures robustness across diverse modeling approaches.
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Membership Inference Attacks (MIA): MIA assesses privacy leakage by attempting infer whether a sample was part of the training dataset. We compute distances (e.g.. Hamming distance) between synthetic samples and the real training set, using threshold- based classifier to predict membership. The attack's success rate (accuracy) indicates privacy risk, with lower values reflecting better privacy protection. This is conducted using the GenHPF representation.
Justification: MIA is a standard metric for evaluating privacy in synthetic EHR data, it measures the risk of re-identifying individuals from the training set, critical concern given EHRs' sensitive patient information.
Time Gap:
Time Gap evaluates the similarity of time interval distributions between consec- utive patient events, using the KS statistic. EHR data, simultameous events (zero intervals) are common due concurrent clinical recordings in clinical workflows temporal resolu- tion constraints of EHR systems. To address this, we compute KS statistics both including and excluding zero intervals, with results excluding zero intervals reported in Table CDFs for both cases are visualized in Figure E panels (a)-(b) show distributions including si- multaneous events, and panels (c)-d) show distributions excluding them. Additionally, absolute event time distributions (from admission to event occurrence) are presented in Figure (f).
Justification: Time Gap ensures synthetic data accurately captures the timing of clinical events, essential for realistic patient trajectories in time-sensitive settings like ICUs. By evaluating both zero and non-zero intervals, it accounts for EHR-specific recording patterns, while the KS statistic provides robust, non-parametric measure of temporal fidelity.
Event Count evaluates the similarity in the distribution of the number of clinical events per patient between synthetic and real EHR data, employing the (KS statistic to compare these distributions. The CDFs of event counts for both synthetic and real data are visualized in Figure 4 (h).
Event Count:
Justiffccaiin: Event Count verifies that synthetic data replicates the frequency of clinical events per patient, key characteristic of patient trajectories.
Next Event Prediction:
Next Event Prediction evaluates the synthetic data's ability to capture temporal sequence dynamics. An LSTM-based model [40] predicts the next event's item or drug name, formulated as a multi-label classification task to handle concurrent events. The model is trained synthetic data and evaluated on real test data using the F1 score, which balances precision and recall for multi-label predictions. Higher Fl scores indicate better sequence modeling.
Justification: This metric captures the sequential patterns of clinical events, essential for modeling patient trajectories. The LSTM's ability to model long-term dependencies suits the complex temporal structure EHRs.
C.3 Visualizations and results
To provide comprehensive view of synthetic data quality, we include the following figures and tables:
Figure Correlation matrix heatmap showing differences in Pairwise Column Correlation (PCC) between real and synthetic data.
Figure Cumulative Distribution Function (CDF) plots for Time Gap (including and excluding zero intervals), absolute event time, and event count distributions.
Table Overall single-table evaluation results, averaged across tables and columns for each dataset.
Tables II Column-wise evaluation results, including CDE, I-CDE, SMAPE, ER gaps for predictive similarity, and null ratio of real and synthetic data.
Table
3
AUROC comparisons for clinical utility tasks across datasets.
Table 10 AUROC results for clinical utility tasks, detailing performance for each task.
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Table 4 Accuracy for Membership Inference Attacks (MIA), Fl scores for Next Event Prediction, and KS statistics for Time Gap and Event Count.
By combining rigorous metric definitions, mathematical formulations, and extensive visualizations, our evaluation pipeline offers a thorough and principled approach to assessing synthetic multi- table EHR data. This comprehensive framework not only addresses existing gaps in evaluation methodologies but also sets a new standard for evaluating time-series data in healthcare applications.
D RawMed framework details
D.1 Architecture details
This section details the architectural components of the RawMed framework, consisting of two distinct modules: event compression and inter-event temporal modeling, providing specifics beyond the main text.
The event compression module transforms serialized clinical event text into compact latent repre- sentations, leveraging a Vector Quantized Variational AutoEncoder (VQ-VAE) [27] or Residual Quantization (RQ) framework [16], both implemented using 1D convolutional neural networks (CNNs). The processing pipeline begins with the construction of input embeddings, followed by encoding, quantization, and decoding stages.
The input embedding, denoted x" E RLXF, integrates three components adapted from an established text-based EHR prediction framework, GenHPF, ensure robust representation of clinical data. The textual embedding (x employs framework-derived embeddings for serialized event text, such as "lab item Glucose value 95 uom mg/dL", tokenized using Bio+Clinical BERT tokenizer [41]. The type embedding (p.)).))) assigns categorical labels to tokens, distinguishing table names, column names, and values to capture the relational structure of the data. The digit-place embedding (Xape,) enhances numeric tokens by tokenizing digits with spaces (e.g.. "123.45" as "I 2 3. 4 5") and assigning position-specific types for integer and decimal places. These components are combined as xf = X + xlyee + X The main text adopts the notation x = x for simplicity, though the full embedding incorporates type and digit- embeddings to enhance structural and numeric fidelity.
The encoder (Enc) processes the input embedding through five CNN layers, each with kernel size of 5, stride of 2, and padding of 2, followed by batch normalization and ReLU activation. This configuration ensures receptive field sufficient to encompass the entire event sequence of length L 128, capturing contextual dependencies. The encoder outputs latent representation. 2f E TR. xF, which is subsequently quantized.
The decoder (Dec) reconstructs the text, type, and digit- position embeddings (K & using transposed CNN layers. The training objective minimizes reconstruction losses for all three embedding components, ensuring faithful recovery of text, type, and digit bigit-position information.
type, X dpe.
The temporal modeling module, implemented as TempoTransformer, is a Transformer-ban beaesrrrer-b ar- chitecture for autoregressive prediction of quantized event sequences interleaved with tokenized timestamps. The model employs causal attention to ensure predictions depend only on prior tokens. Positional encodings provide sequence order information, and the model is trained from scratch, as the autoregressive target is the quantized codebook, obviating the need for fine-tuning from pretrained weights.
D.2 Postprocessing
This appendix elaborates on the two-stage postprocessing pipeline applied convert synthetic patient trajectories into relational tables, aligning with the structural and semantic consistency of real data, The first stage, converting text to tabular format, involves the core steps of event-level verification and patient-level validation (Algorithm Tb. The second stage, enhancing data quality, applies additional column-specific constraints to refine the synthetic tables' statistical fidelity, as referenced in Section 3.4
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Column-Specific Constraint Enforcement After validated sequences are converted into relational tables, further postprocessing ensures compliance with column- -ppuuiic constraints, addressing potential errors from the generative process (e.g., quantization or autoregressive sampling). The following steps are applied:
Numeric Columns: Values are filtered to lie within the minimum and maximum ranges observed in the real data, applied at both the column and item levels. Non-compliant events (e.g., a glucose value outside the real data's s range) are removed.
Invalid values not present in the permissible values for each column are replaced with the closest valid value, determined using Levenshtein distance similarity. A distance threshold ensures that only sufficiently similar replacements accepted; otherwise, the value is flagged as invalid.
Categorical Columns:
Patient-Level Filtering: If any events contains numeric value outside the valid range or categorical value exceeding the Levenshtein distance threshold, the entire patient sample associated with that row is removed to maintain data integrity.
These steps ensure that the final relational tables adhere to the structural and statistical characteristics of the real data, removing outliers and ensuring both numerical and categorical validity.
Modification and Rejection Rate Analysis To quantify the impact of our validation protocols, we analyzed the modification and rejection rates across both postprocessing stages for the eICU and MIMIC-IV datasets.
During the initial stage, converting text tabular format, 0.42% of events were modified and 0.07% were rejected in eICU, For MIMIC-IV, these rates were 0.73% and 0.17%, respectively. At the patient level, however, rejection rates were notably higher, reaching 2. 7.%1% for eICU and 18.23% for MIMIC-IV.
In the subsequent stage, enhancing data quality, stricter column-specific constraints were enforced. This led to event-level rates of 1.39% modified and 0.49% rejected for eICU, and 1.07% modified and 0.79% rejected for MIMIC-IV. These stringent checks led to a substantial increase in patient-level rejection rates, which rose to 16.90% in eICU and 54.53% in MIMIC-IV.
The pronounced disparity between patient-level and event-level rejection rates is a direct consequence of our stringent data integrity policy. A single erroneous event necessitates the rejection of the entire patient trajectory. This effect is particularly notable for datasets with long sequences, (e.g.. with hundreds of events), all of which must be accurate for the trajectory to be retained. We emphasize that this full poose-sscssiing is crucial for RawMed's 's data integrity. We do not recommend partial post-pre post-processing, as it fundamentally compromises the quality the synthetic data.
E
Training details and hyperparameters
E.1 Generative models
RawMed The framework employs a two-stage training process: the event compression module is trained first to generate quantized representations, followed by the inter-event temporal modeling module to predict sequences autoregressively.
Event-level compression: The event compression module uses the AdamW optimizer (learning rate: 5c-4, weight decay: 0.01), processing batches of 4096 events for up to 200 epochs, with early stopping after 10 epochs of stagnant validation accuracy. The loss function combines reconstruction losses for text, type, and digit-place embeddings with a commitment loss (commitment cost: 1.0). An EMA decay factor of 0.8 is applied for codebook updates in VQ-VAE training. dropout rate of 0.2 is used for regularization. The input embedding has sequence length L = 128 and embedding dimension F = 256, compressed to latent representation with L = and F. 256. The codebook contains K 1024 entries, with residual quantization (RQ) using depth of D 2, yielding 4 x 2 code representation. Training is performed on single
NVIDIA A6000 GPU, completing in under 24 hours.
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Temporal modeling between events: The temporal modeling module uses the A optimizer (learning rate: 3e-4, weight decay: 0.01), with batches of 32 sequences for up to 200 epochs, with early stopping after 10 epochs without improvement. The TempoTrans ormer consists of 12 layers, each with 8 attention heads, hidden dimension of 512, and feed-forward dimension of 2048. The input sequence includes i
time tokens (7," E .O..... 9)², length 2), event tokens (k." E [1024] 202444x2, codebook size 1024), and special tokens (<SOS>, <EOS>, <PAD>). resulting in vocabulary size of 1037. Training was conducted on three NVIDIA A6000 GPUs for MIMIC and on single NVIDIA A6000 GPU for eICU, both completing in under 48 hours.
Generation: Data generation uses top-k sampling with k = 250 for MIMIC-IV and = 150 for eICU.
RealTabFFormr 255 Text-based autoregressive model for generating relational database. This model involves fine-tuning the Meta-LLaMA-331-80 model and generation step using top-p sampling.
Fine-tuning: The Me3--LLaMA-33188 model 32 was fine-tuned using QLoRA 31 (rank r=128, LoRA scaling factor a=128, LoRA dropout=0.05) and Flash Attention-2 for memory optimization. Training used a maximum sequence length of 11,240 tokens, matching the input data. The Adafactor optimizer
applied with a learning rate of 2e-4, weight decay of 0.001, a batch size of 1, gradient accumulation (step size=4), and gradient checkpointing. Training ran for 2 epochs on single RTX 3090 GPU, taking approximately 10 days for MIMIC-IV and 20 days for eICU.
Generation: Used the fine-tuned Meta-LLaMAA3311 model with top-p sampling, setting the temperature to I and threshold of 0.7.
The Synthetic Data Vault (SDV) uses the Hierarchical ML Algorithm (HMA) Synthesizer to generate synthetic relational data, modeling individual tables with Gaus- sian Copulas while preserving parent-child relationships. The SDV framework's default hyperparameters are applicd for training.
SDV 344:
RC-TGAN: 355 A GAN-based model designed to generate synthetic relational databases by modeling parent-child relationships.
Training and generation: Uses default hyperparameters from the official repository, with batch size set to 500,000, the maximum feasible value for the NVIDIA RTX
3090 GPU (default: 500). Other hyperparameters include embedding dimension: 128, generator dimensions: (256, 256), discriminator dimensions: (256, 256), epochs: 300, discriminator steps: generator learning rate: 2e-4, and discriminator learning rate: 2e-4. Hyperparameter search tested discriminator steps ([1, 3J). generator learning rate ([le-4, 2e-4, 4e-4]), and discriminator learning rate ([1e-4, 2c-4, 4e-4]), but defaults performed best. Training took less than 10 hours on a single NVIDIA RTX 3090 GPU without early stopping.
ClavaDDPM: (36]
diffusion-basse based model for multi-table data generation, Cluster Latent Variable guided Denoising Diffusion Probabilistic Models (ClavaDDPM) leverage Denoising Diffusion Probabilistic Models (DDPMs) to model complex tabular data distributions. By using clustering labels as intermediaries, ClavaDDPM captures Long-range dependencies across interconnected tables, particularly through foreign key constraints.
Training and generation: We adopted the default hyperparameters from the official ClavaDDPM GitHub, featuring diffusion model with six MLP layers ([512, 1024, 1024, 1024, 1024, 512]). a learning rate of 6e-4, and batch size of 4096 for 100,000 iterations with cosine scheduler. The model employs 2000 timesteps with MSE loss and uses clusters for clustering. Training was completed in under hours on single NVIDIA A6000 GPU.
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end end end Add (t,, (( to R'[r] valid event count valid _vent count + end Algorithm 1: Postprocessing for Relational Table Construction
Input: Set of patients P, Text-based events E, timestamps T, valid column names C, valid table names &, observation window Tmux
Output: Relational tables R
Initialize R + dict of r - list (a dictionary mapping table names to lists of event records)
Initialize Psalid <
for each patient E P do
Initialize R' <- dict of + list (temporary tables for patient p)
Initialize valid _eve__eeett___ count t 0
for each event ex E with timestamp t T do
Parse eº into table name and column-value pairs {(c )f
if r $ then Continue
end
e does not conform to column-value pair format then Continue end
for each (c R.jo) ((dgg )} do
if
then
t arg mineec Levenshtein(c,,,
end
if
c. is numeric column and vi, contains non-numeric characters then o + RemoveNomNumericc (v ".j
if is numeric then
Continue
valid_ cecid__vee__ooott count == EP) then Add to Pvalid Add ['[r] to R[T]
end
end
Sort R by timestamp in ascending order for each patient p P valid do
for each event (1% {(e
) in R do
if >
and 1 < 1
4-1 or 1 > Taax then
Discard (1,, ((c)
((
u ()) and all events (1%, (ch "k for k 2 i from
Break
end
end
end
return
E.2
Predictive models for evaluation
We use the following predictive models to assess the quality of synthetic data:
E.2.1
Clinical utility
GenHPF addresses the heterogeneity of multi-table EHR data by transform- ing patient events into single hierarchical textual sequence. This method sequentially lists events such as medications, infusions, and lab results in chronological order, requiring
GenHPF 15]
29

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Table 10: Summary of 11 ICU prediction tasks using synthetic EHR data. Multi-class tasks predict binned lab values, while binary tasks classify whether specific medications are prescribed. Predictions made at the midpoint (7 (2 of observation window of length T (e.g., T = 12 hours) forecast the last lab value or medication event occurrence in the second half.
Task Name
Source
Task Type
Creatinine (Cr)
Lab
Multi-class
Platelets (Plt)
Lab
Multi-class
White Blood Cells (WBC)
Lab
Multi-class
Hcmoglobin (Hb)
Lab
Multi-clas.
Bicarbonate (HCO3)
Lab
Multi-
Sodium (Na)
Lab
Multi-class
Magnesium Sulfate (MgSO4)
Medication
Binary
Heparin (Hep)
Medication
Binary
Potassium Chloride (KCI)
Medication
Binary
Norepinephrine (NE)
Input
Binary
Propofol (Prop)
Input
Binary
minimal preprocessing. GenHPF enables predictive models to capture the full complexity of patient records, making it suitable for multi-task learning without extensive domain- specific feature engineering. Its flexibility supports applications across diverse EHR systems, enhancing model generalizability for downstream predictive tasks.
Training: GenHPF was trained with embedding dimension of 1 128, 4 attention heads, and 2 transformer layers. uses batch size 64, a dropout rate of 0.1, and learning rate of 5e-5 for 50 epochs. Training occurred on single NVIDIA A6000 GPU, targeting multi-task prediction for EHR lab tests and medications, with early stopping after 10 epochs of no improvement.
BEEDS-TAB is an automated tabularization tool that converts Medical Event Data Standard (MEDS) formatted EHR data into standardized tabular representations by aggregating events into fixed time intervals. It processes irregularly sampled time-series data, supporting various aggregation functions (e.g.. sum, count, mean) and window sizes to handle diverse data types, including static codes, numerical values, and time-series demonstrated by processing 500 patients from
MEDS-TAB (38
events. With high scalability and MIMIC-IV in 16 seconds with 1,410MB memory Bange--EDS-TTE TAB simplifies predictive modeling by providing structured inputs compatible with machine learning algorithms like XGBoost, achieving competitive performance in clinical tasks.
- Training: MEDS- was trained using the default hyperparameters from the official MEDS-TAB GitHub repository, employing XGBoost for predictive modeling. In contrast to GenHPF, it trains on individual tasks rather than multi-task learning.
E.2.2 Predictive similarity
For predictive similarity evaluation, the XGBoost model was configured with 100 estimators, subsample ratio of 0.9, and a maximum bin size of 256. Depending on the task, objectives were set to binary:logistic for binary classification, multi:softmax for multiclass classification, or reg:squarederror for regression, with evaluation metrics logloss, mlogloss, or rmse, respectively.
E.2.3 Next event prediction
For the Next Event Prediction task, we employ single-layer LSTM with 128 hidden units, with input and output sizes equal to the number of classes. A sigmoid activation function is used for multi-label classification. The model trained with the Adam optimizer a learning rate of 0.001, employing a weighted binary cross-entropy loss (with inverse log-frequency weights normalized to mean of 1), batch size of 256, and 50 epochs. classification threshold of 0.1 is applied to enhance recall for imbalanced classes.
30

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Table 11: Column-wise fidelity for MIMIC-IV. Columns prefixed with "L" (laboratory measure- ments), "I" (infusions), or "M" (medications). Types "C" and "N" denote categorical and numerical columns, respectively. TRTR on-Real) denote error rates for categorical columns and SMAPE for numerical columns, reported + standard deviation (std) across three random seeds, assessing predictive similarity task performance.
and TSTR
Test-
CoL. Name
Type
CDE
I-CDE
Real Null (%)
Syn Null (%)
TRTR
TSTR
L/iemidd
0.06
0.00
0.00
66.08 124.72
78.87
-23.05
1/value
C
11.07
0.14
5.78
4.51
0.00 #0.00
0.77 #0.02
L/valueuom
C
0.04
0.03
13.78
12.49
0.07
10.00
2,50
1:002
L/flag
0.00
62.27
64.18
1. Upriority
C
0.03
0.04
27.34
29.48
44.16
44.32 #0.
L/comments
C
0.11
0.22
83.41
86.15
57.34 10.61
63,52 12204
L/valuenum
0.01
0.06
11.13
9.46
116.64 +0.76
86,37 11.99
[Jref_range Le__aann__laaae lower
=0020
L/ref raper_upper
N
0.01
0.01
19.04
17.15
30.45
30,01 +0.36
Witemid
C
0.09
0.00
0.00
28.17 #3.03
29.19 10.13
V/aouuttuo
0.02
0.00
0.00
0.07 #0.00
0.13 #0.00
Urateuom
37.49
39.54
0.02
0.07 +0.00
Vordereatteoryynmme
0.03
0.03
0.00
0.00
0.16
0.19 +0.00
Isccondaryordercattggg
0.02
0.01
25.82
26.57
0.00 #0.00
0.00 #0.00
Vandercomppaenttyyddocppiiio
0.01
0.01
0.00
0.00
0.00 #0.00
0.01 #0.00
Jordereueegorydeerriitoooo
0.00
0,00
0.00
0,00
Itotalamountioo
0.00
0.00
12.35
13.59
Visopenhag
C
0.01
0.01
0.00
0.00
0.00 #0.00
0.00 +0.00
l/moount
0.08
0.00
0.05
88.26 +0.87
87.75 #:2.87
Urate
0.13
37.49
39.54
36.47
62.34 +
Jpatientweight
N
0.09
0.00
0.00
21.23 10.01
21.78 1.0.2
Utoralamount
N
0.02
0.07
12.38
13.63
6.77 20.08
8.81 #:0.02
Voriginalamount
0.12
0.17
0.10
28.00 11.23
30,78 1.0.4
Voriginalrate
N
0.09
5.83
4.46
41.72
48.13
Midrug
C
0.08
0.00
0.00
31.00
40,53 11.01
M/drug_type
C
0.00
0.00
0.00
0.00
0.82 10.07
1.41 10.04
M/prod_sstennit
strength
0.05
0.09
0.04
17.69 10.63
37.79 1:043
Mídose _et___un________________________
0.03
0.02
0.09
0.04
5,93
13,06
M/form_uni Baaemnuntit_iee disp
C
0.03
0.04
0.09
0.05
8.16 10.21
21.07 10.45
M/route
C
0.04
0.04
0.09
0.04
31,99 10.49
40.59 1:030
Mídoses_ per_pepr_____0 22__rrs
59.97
61.97
60.30
10.11
60.36
1.0
Mídose val
0.02
0.04
6.13
6.38
109.53
105.76
M/form_val siii_vll__ha
N
0.02
0.03
5.59
5.99
129.93
133227
82,49 14.43
F
Additional experimental results
F1
Column-wise fidelity metrics
We report column-wise fidelity metrics for RawMed-generater data on the MIMIC-IV and eICU datasets, evaluating distributional similarity and predictive performance across categorical and numerical columns. Notably, RawMed-generatee null ratios closely align with real data, as detailed in Tables and
F.2 Task-specific predictive performance
We report task-wise predictive performance for clinical tasks using RawMed-generated data on the MIMIC-IV and eICU datasets, comparing Synthetic-Test-on-RRad (TSTR) metrics, as detailed in Table [3
(TRTR) and Train-on-
F.3 ED dataset generalizability
To assess the generalizability of RawMed across diverse clinical environments, we evaluated its performance on the Emergency Department (ED) dataset from MIMIC-IV-ED, applying the same methodology and evaluation framework as used for the ICU dataset.
31

---

Table 12: Column-wise fidelity for eICU. Columns are prefixed with "L" (laboratory measurements), "T" (infusions), or "M" (medications). Types "C" and "N" denote categorical and numerical columns, respectively. TRTR -eaan-oon-ell--esse Realll-- and TSTR -asiinoo--Snthttii--- Test-on-Reet denote error rates for categorical columns and SMAPE for numerical columns, reported as mean + standard deviation (std) across three random seeds, assessing predictive similarity task performance.
Col. Name
Type
I-CDE CDE
Real Null (%)
Syn Null (%)
TRTR
TSTR
L/lannamc
C
0,04
0.00
0.00
52.57 #3.92
53.10
12.26
Jlabmeasurenamesssseen
C
0.03
0.07
5.24
4.82
0.07
40.02
0.59
#0.07
Vlabmeesuurenameintttteee
C
0.04
0.14
6,37
5,59
54.97
1:030
49.94
1/aahtyped
C
0.03
0.06
0.00
0.00
0.00
+0.00
0.28
#0.04
L/labresult
N
0.01
0.11
0.93
0.64
14.68
40.55
24.54
#0.65
L/labresulttext
N
0.01
0.12
0.75
0.54
19.72 40.46
23.40
00.35
Vdrugnaam
C 0.16
0.00
0.00
57.38 +0.32
60.48
20.20
Vinfusionrate
N
0.03
0.43
55.54
63.14
4.027 77.70
77.95
1100
Idrugamount
N 0.06
0.32
67.48
74.43
71.46
1.557
69.29 10.18
Vvolumeofflii
N 0.03
67.47
74.37
14.80
20.47
18,11 10.18
/pttientweight
N 0.03
90.91
95,91
12.49 40.20
33.41 20.44
Idrugrate
N 0.03
0.36
0.46
0.55
93.12
92.25
Midrugname
C 0.10
32.53
29.65
73.75
81,10
Midrugivadmixture
C 0.02
0.03
0.00
0,00
14.49 10.07
16.36 #0.01
Mídosage
C 0.07
0.15
11.13
11.38
80.19 +0.08
81.74 10.17
Mfrouteadmin
C
0.07
0.11
0.00
0.01
55.23 40.15
57.59 #0.09
M/frequency
C
0.08
0.22
12.60
11,31
85.52
#0.19
88.22 #0.10
M/pm
C
0.00
0.06
0.01
0.08
10.40
#0.12
11.97 #0.10
Table 13: Clinical task utility evaluation.
(TSTR) performance for MIMIC-IV and eICU datasets, reported as mean + standard deviation (std) across three random seeds.
TRTR denotes Train-on -aee---oo-------ie Rea---eet-------eeee-- Real, and TSTR denotes Baain-oo-Synthetii wentheeic-Test-nn--- GenHPF and MEDS-TAS TAB are synthetic data generation methods. Tasks include laboratory value predictions (Creatinine: Cr, Platelets: Plt, White Blood Cells: WBC. Hemoglobin: Hb, Bicarbonate: HC03, Sodium: Na) and medication/input predictions (Magnesium Sulfate: MgSO4, Heparin: Hep,
Potassium Chloride: KCI, Norepinephrine: NE, Propofol: Prop).
MIMIC-IV
eCCU
GenHPF
MEDS-TAB
GenHPF
MEDS- TAB
Task
TRTR
TSTR
TRTR
TSTR
TRTR
TSTR
TRTR
TSTR
Cr
0.89 =0.00
0.88
+0.00
0.97
=0.00
0.95
=0.00
0.86
=0.00
0.83
#0.00
0.94 =0.00
0.89 =0.00
Pit
0.88 20.01
0.87
20.00
0.96 #0.00
0.95
20.00
0.84
20.00
0.83
#0.00
0.94
#0.00
0.91 #0.00
WBC
0.78 =0.01
0.73
#0.03
0.91 #0.00
0.86
=0.00
0.73
#0.01
0.70
#0.02
0.87 =0.00
0.80 =0.00
HB
0.75 20.01
0.74
#0.01
0.85 #0.00
0.82
#0.00
0.74
#0.00
0.72
#0.01
0.81 =0.00
0.78 #0.00
НС03
0.79 20.01
0.79
#0.01
0.90 =0.00
0.90
=0.00
0.78 #0.01
0.76 #0.00
0.88 =0.00
0.86 +0.00
0.80 20.01
0.78
20.01
0.93 #0.00
0.92 20.00
0.80 #0.01
0.79
#0.01
0.91 #0.00
0.90 #0.00
MgS04
0.81 10.02
0.81 10.01
0.86 10.00
0.83 10.00
0.73 10.01
0.72 10.01
0.81 10.01
0.75 10.00
Hep
0.68 10.01
0.65 10.00
0.80
10.00
0.74 10.00
0.65 10.00
0.63
10.01
0.72 10.00
0.66 10.00
KCI
0.73
0.70
0.80
10.00
0.73
0.74 10.01
0.66
0.77 10.00
0.68 10.00
NE
0,96 +0.00
0.96
+0.00
0.96
=0.00
0.96 +0.00
0.96 +0.00
0.95
=0.00
0.95 =0.00
0.94 =0.00
Prop
0,94 20.00
0.94 +0.00
0.95 #0.00
0.94 #0.00
0.96 =0.00
0.95 #0.00
0.96 +0.00
0.94 #0.00
Avg
0.82 10.09
0.80 10.09
0.90 10.66
0.87 10.08
0.80
10.09
0.78 10.10
0.87
10.08
0.83 10.10
Predietive task definition Due to differences in clinical context, the ICU task definitions were not directly applicable to the ED dataset. Instead, we designed binary classification task to evaluate the utility of synthetic data, based on four clinically relevant features: heart rate, respiratory rate, morphine administration, and ondansetron administration. The observation window was set to T = 6 hours, split into two 3-hour segments. The first hours serve as input for the predictive model, with predictions made at the midpoint (T/2 3 hours). Binary labels were defined as follows, using the maximum value within the second 3-hour period for heart rate and respiratory rate, and occurrence for medication administration:
32

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Table 14: Comparison VQ-VAE and EQ-VAE on MIMIC-IV and eICU, reporting Column- wise
Density Estimation (CDE), Pairwise Column Correlation (PCC), Time Gap, and Event Count metrics.
MIMIC-IV
eICU
Metric
vQ
RQ
VQ
RQ
CDE +
0.05
0.04
0.04
0.05
Categorical
0.05
0.04
0.05
0.06
Numerical
0.05
0.03
0.03
0.03
Patientweight
0.28
0.09
0.03
PCC I
0.04
0.04
0.07
0.06
Time Gap 4
0.05
0.01
0.03
0.03
# Events
0.13
0.02
0.03
0.05
Table Scalability results on MIMIC-IV and eICU for 6-hour and 24-hour observation windows, reporting Column wise Density Estimation (CDE), Pairwise Column Correlation (PCC), Time Gap. and Event Count metrics.
MIMIC-IV
eCCU
Obs. size
24h
6h
24h
CDE +
0.04
0.05
0.06
0.06
PCC
0.03
0.02
0.09
0.07
Time Gap +
0.05
0.07
0.01
0.02
# Events 4
0.07
0.11
0.08
0.01
Heart rate (beats per minute, bpm): the maximum value is 2 120 bpm (indicating tachycardia, above the normal adult range of 60-100 bpm). 0 otherwise.
Respiratory rate (respirations per minute, rpm): the maximum value is > rpm (indicating tachypnea, beyond the normal adult range of 12-20 rpm), 0 otherwise.
Morphine administration: administered, 0 not.
Ondansetron administration:
1 if administered, 0 if not.
These thresholds were selected based established clinical criteria identify critical conditions in the ED setting.
Results The evaluation demonstrated that RawMed generalizes effectively to the ED dataset, achiev- ing robust performance across predictive tasks. Table [6 compares RawMed with baseline models (i.e., SDV, RC-TGAN, ClavaDDPM), reporting fidelity metrics (Column-wise Density Estimation, CDE; Pairwise Column Correlation, PCC; Time Gap; Number of Events) and clinical utility. RawMed surpasses baselines in most metries, with CDE of 0.08, PCC of 0.02, Time Gap of 0.02, Number of Events error of 0.02, and a Utility score of 0.79, nearing the real data's Utility of 0.83. Despite differing clinical environments, RawMed's performance closely matches real data utility and surpasses baselines, indicating potential for generalization to other datasets.
F4 Conditional generation
We conducted experiments on conditional generation by incorporating static features into the Temporal Modeling framework (Section 3.3) Specifically, we integrated age, gender, and admission diagnosis (or type) as static features. During training. these features were prepended the sequences, enabling the model to learn the combined sequence auto-regressively. For generation, the model utilized real static features as conditions to produce the complete sequence. further investigate conditional generation, we explored the inclusion of initial medical context, providing the model with real static features and approximately one-fourth of the initial real-data events from the sequence, tasking with generating the remaining thrce-fourths.
33

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Table 16: Model Performance Comparison on MIMIC-IV-ED, reporting Column- wise Density Estimation (CDE), Pairwise Column Correlation (PCC), Time Gap, Event Count, and Clinical Utility (AUROC) metrics. The best results are bold.
Model
CDE + PCC
Time Gap + # Events +
Utility
Real
0.83
SDV
0.05
0.21
0,46
0.07
0.63=0.09
RC-TGAN
0.55
0.20
0.34
0.06
0.5710.06
ClavaDDPM
0.16
0.11
0.35
0.06
0.6410006
RawMed
0.08
0.02
0.02
0.79100.05
To assess whether the generated sequences effectively captured the relationships with static features, we trained three classifiers predict each age, gender, admission diagnosis or type from the generated sequences, excluding the static feature portion itself. For this classification, age was binned, and for the admission diagnosis, only four specific categories from the 'Non-operative Organ Systems' hierarchy (which account for the majority of diagnoses) were selected, namely cardiovascular, neurologic, respiratory, and gastrointestinal.
The performance of our conditional generation approach on the MIMICIIV and eICU datasets presented in Table 17 For the MIMIC-IV dataset, the synthetic data demonstrates perfor- mance comparable to real data, indicating that our model effectively captures the relationship between static features and sequential data. Notably, conditioning on initial events enhances performance, with the AUROC for age prediction increasing from 0.75 to 0.77. For gender prediction, both real and synthetic data achieved an AUROC of 1.00. This attributable to the inclusion of all columns from the labevents table in synthetic data generation, which unintentionally incorporates gender- specific reference ranges in lab values (e.g.. creatinine, hemoglobin) within the refgre _ower and ref_ range upper columns, serving as direct gender indicators.
Results
On the eICU dataset, similar trends were observed, with synthetic data approaching the performance of real data, particularly when leveraging initial medical context. The AUROC values improved when conditioning on initial events, rising from 0.65 to 0.69 for age, 0.52 to 0.59 for gender, and 0.86 to 0.88 for admission diagnosis, compared to static features alone. These results underscore the flexibility of our framework in supporting conditional generation with richer feature sets, enhancing synthetic data quality. Although gender prediction on eICU exhibited slight performance drop compared to real data, we anticipate improvements through further methodological refinements and hyperparameter optimization. The results from both datasets validates the flexibility and potential of our approach for conditional generation.
Table 17: Performance (AUROC) of conditional generation models on MIMIC-IV and eICU datasets.
Adm. Type refers to Admission Type, and Adm. Diag. refers to Admission Diagnosis.
MIMIC-IV
cICU
Model
Age
Gender
Adm. Type
Age
Gender
Adm. Diag,
Real
0.80
1.00
0.92
0.72
0.67
0.89
RawMed (static only)
0.75
1.00
0.89
0.65
0.52
0.86
RawMed (static+1/4 initial events)
0.77
1.00
0.89
0.69
0.59
0.88
34

---

ECB 231
ane
MIMIC-IV
(b) MIMIC-IV med
(c) MIMIC-IV/ input
4.29
042
60
231
0.
GM
S53
CN
000
401
0.2
240
60
Coe
205
414
0.3
612
0.0
52T
AGL
600
501
644
440
(d) eICU lab
(e) eCCU / med
(f) cCCU / input
Figure 3: Table-specific visualizations of absolute differences in Pairwise Column Correlation (PCC) matrices between real and synthetic data for MIMIC-IV and eICU tables.
35

---

1.
1.
0.4
0.8
0.4
0.6
0.1
0.1
0.2
0.2
Real
Renl
0.2
:00
200
302
420
200
500
700
120
200
320
400
300
500
700
(a) MIMIC-IV Time Gap (w/ Sim. Events)
(b) cICU Time Gap (w/ Sim. Events)
1.2
0.5
0.1
RawMed
RawMed
160
202
360
1.0
460
500
600
706
(c) MIMIC-IV / Time Gap (w/o Sim. Events)
(d) cICU / Time Gap (w/o Sim. Events)
Real RawMed
Real RawMed
0.2
200
MIMIC-IV Absolute Time
(f) eICU Absolute Time
1.2
1.2
0.8
0.8
0.5
0.5
0.1
0.4
0.2
0.2
Real
Ravll
0.2
0.-
RawMed
32
102
350
220
250
22
40
62
80
100
(g) MIMIC-IV /Event Counts
(h) elCU Event Counts
Figure 4: Cumulative Distribution Functions (CDFs) of real and synthetic data for MIMIC-IV and eICU datasets, showing Time Gap (with and without simultaneous events), Absolute Time from admission, and Event Counts per patient.
36


Reviews:
[{"review_id": "mU2PauLL20", "reviewer": "Reviewer_Wqvt", "summary": "This paper proposed a framework, named RawMed to generate raw multi-table time-series EHRs and also introduce a evaluation framework for the synthetic raw EHRs which covers all features of the raw EHRS. Experiments on two public datasets validate the proposed method.", "questions": "refer to strength and weakness", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The major strength is that the paper is the first work designed to synthesize raw, multi-table time-series EHR, and requiring minimal preprocessing in the method (e.g., compression module).\n2. The corresponding eval framework is novel covering the complex temporal and inter-table dynamics.\n3. The experimental validation is thorough and convincing.\n\nWeakness:\n1. As discussed in the paper, the paper only targets on limited scope of the tables used in the experiments, not sure if it will be able to scale up.\n2. The handling of static patient attributes is a limitation, which doesnot explicitly model static features like gender or age.\n3. Post-processing steps are still needed in the work to ensure the structural and temporal integrity of the generated data.", "was_revised": true, "final_justification": "The authors addressed most of my concerns, considering the paper's novelty and promising results, I keep my score as 4.", "rebuttal": "**Response to Weakness1&2:**\n\nWe sincerely appreciate your insightful feedback particularly in highlighting the current limitations regarding the scope of tables and the handling of static patient attributes. As we explicitly discussed in the Section 6 of our paper, these are the current limitations of RawMed. In this initial stage of our research, our primary focus was to establish the model's fundamental generation capabilities. We acknowledge these as key areas for future work, aiming to scale up to dozens of tables, including those with static features.\n\nDuring the rebuttal period, we conducted preliminary experiments on conditional generation incorporating static features. Specifically, we integrated age, gender, and admission diagnosis into the Temporal Modeling (Section 3.3).\n\nFor training, we prepended these static features to the sequences, and the model learned the combined sequence auto-regressively. During generation, the model uses real static features as a condition to produce the complete sequence.\n\nTo further explore conditional generation, we also experimented with providing initial medical context as a condition. This involved providing the model with real static features and approximately one-fourth of the initial real-data events from the sequence, with the model then generating the remaining three-fourths of the sequence.\n\nTo evaluate if the generated sequences effectively captured the relationship with static features, we used three classifiers to predict each static feature (age, gender, admission diagnosis) from the generated sequences, excluding the static feature portion itself. Age was binned for this classification. Model performance was evaluated using micro-AUROC for multiclass classifications (age and admission diagnosis) and AUROC for binary classification (gender).\n\n| Model                          | Age  | Gender | Admission Diagnosis |\n|--------------------------------|------|--------|---------------------|\n| Real                           | 0.72 | 0.67   | 0.89                |\n| RawMed (static only)           | 0.65 | 0.52   | 0.86                |\n| RawMed (static+1/4 initial events) | 0.69 | 0.59   | 0.88                |\n\nThese results show comparable performance for age and admission diagnoses between real and synthetic data, especially when leveraging the initial medical context. The increased AUROC values when conditioning on a portion of initial events (e.g., 0.69, 0.59, 0.88 for age, gender, and admission diagnosis, respectively, compared to 0.65, 0.52, 0.86 with static features alone) experimentally confirm our framework's flexibility in effectively supporting conditional generation using a richer set of features, leading to improved synthetic data quality. While we observed a slight drop in gender prediction compared to real data, we believe there's room for improvement with further methodological enhancements and hyperparameter tuning, given the short experimental timeframe. Due to these constraints, experiments were limited to the eICU dataset; we will update with MIMIC-IV results (for age, gender, and admission type) during the discussion period.  \n\n\n---  \n\n\n**Response to Weakness3:**  \nThe need for post-processing steps to ensure the structural and temporal integrity of generated data is a valid and widely accepted practice for quality assurance across various domains. These steps are (1) inevitable, as an autoregressive generation—predicting tokens sequentially—inherently accumulates errors over time, making it challenging to produce a 100% flawless sample in a single pass. For example, the text-based tabular synthesis model GReaT [1], which relies on autoregressive generation, discards samples that violate required formats. Moreover, they are (2) practical, as even renowned generative models in the image domain, such as VQGAN [2] and VQ‑VAE2 [3],use classifier-based rejection sampling to filter out low-quality samples and improve overall sample quality.  \n\n[1] Language Models are Realistic Tabular Data Generators (Borisov et al., ICLR 2023)  \n[2] Taming Transformers for High-Resolution Image Synthesis (Esser et al., CVPR 2021)  \n[3] Generating Diverse High-Fidelity Images with VQ-VAE-2 (Razavi et al., NeurIPS 2019)"}, {"review_id": "XewGtQ7BTg", "reviewer": "Reviewer_4p1X", "summary": "This paper presents RawMed, designed specifically for multi-table time-series Electronic Health Records (EHRs). Unlike previous methods that rely heavily on feature selection and complex preprocessing, this work synthesizes raw, minimally processed EHR data, including all original table columns and values. It uses a text-based representation of EHR events and compresses these representations into a latent space via Residual Quantization (RQ), enabling efficient autoregressive generation.", "questions": "See above,", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n- This study addresses an important, under-explored challenge: generating multi-table, time-series EHR data with minimal preprocessing, preserving original values and database schema integrity.\n- The paper introduces a well-rounded evaluation framework that thoroughly assesses synthetic data across multiple critical dimensions\n\nWeakness:\n- The evaluation is limited to only two datasets (MIMIC-IV and eICU). The generalizability and robustness of RawMed would benefit from additional evaluation on more diverse datasets. \n- The compression and subsequent decompression of event embeddings may introduce distortions or inaccuracies. I am curious whether the compressed time series representation can be decoded to the original information. For example, if users ask about the height of a patient, can the model answer it?", "was_revised": false, "final_justification": "", "rebuttal": "**Response to Weakness1:**  \nWe appreciate the reviewer’s concern regarding the evaluation of RawMed on only two datasets (MIMIC-IV and eICU) and the suggestion to include more diverse datasets to assess generalizability. However, as noted in Section 4.1 (Setup: Dataset, lines 201–203 in the manuscript), RawMed was also evaluated on the MIMIC-IV-ED dataset, which represents emergency department settings and provides a distinct clinical context compared to the ICU-based MIMIC-IV and eICU datasets. The results, reported in Appendix F.3 and Table 16, show that RawMed’s performance closely matches real data utility and outperforms baseline methods, indicating its potential for generalization to other datasets despite different clinical environments. \n\n**Response to Weakness2:**  \nThank you for your insightful comments. To empirically quantify the deviation from original information that might occur during the compression of event embeddings, we measure the token-level reconstruction accuracy on the test set, reaching 98.2% for MIMIC-IV and 99.8% for eICU. This confirms our compression introduces only negligible distortion, barely affecting the subsequent autoregressive decoding stage, while considering the benefits of compression for text-based embeddings.\n\nAs detailed in 3.2 and 3.3, our method compresses individual event representations, not the entire time-series representation. Therefore, our model does not involve decoding a compressed time-series back to its original form. Instead, we focus on reconstructing each individual event representation.\n\nRegarding your last question about querying patient height, we would greatly appreciate it if you could elaborate on your question. Our model is designed for synthetic EHR generation, not for question-answering tasks, and is not required to address such direct inquiries."}, {"review_id": "5037VpPdWY", "reviewer": "Reviewer_tPMX", "summary": "This paper introduces RawMed, a framework for generating synthetic electronic health records (EHRs) that preserves the multi-table relational structure and time-series nature of raw EHR data. \n\nPrevious approaches that generate only selected features with extensive preprocessing, RawMed represents EHR data as text, employs Residual Quantization for compression, and uses an autoregressive model to capture temporal dynamics. \n\nThe authors conduct comprehensive evaluation on MIMIC-IV and eICU dataset, showing that RawMed outperforms baseline methods in fidelity, utility, and privacy protection.", "questions": "1. Could the authors provide more insight into the errors that require postprocessing? Understanding the nature of these errors might reveal fundamental limitations of the text-based approach.\n2. How might conditional generation be incorporated into the framework? This would significantly enhance the practical utility of the approach. Being said, can this approach extended to hybrid generation mode with user preferences? \n3. Information Loss: When compressing raw EHR data using RQ, what is the quantitative information loss measured across different column types? And what does that mean?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\nS1: Novel problem formulation\n\nThe paper addresses an important gap in synthetic EHR generation by focusing on raw multi-relational table rather than preprocessed tables, which increases the accessibility of synthetic generation for general users.\n\nS2: Technical innovation\n\nThe combination of text-based representation, compression via Residual Quantization, and temporal modeling creates an elegant solution to a complex problem. The authors demonstrate why RQ is superior to VQ for their task through careful ablation studies.\n\nS3: Strong empirical results\n\nRawMed consistently outperforms baselines across metrics\n\n\n**Weaknesses**\n\nW1: Lack of conditional generation\n\nThe method generates data unconditionally, whereas many practical applications would benefit from the ability to generate synthetic EHRs conditioned on specific patient characteristics or medical conditions. It would be beneficial to conduct small conditional generation tasks\n\nW2: If post-processing makes the problem complicated?\n\nIn the paper, it says \"These text-based events and timestamps are then converted to relational tables.\" Is this step heuristic? Could authors explain and investigate the robustness of algorithm 1 in appendix.", "was_revised": true, "final_justification": "Authors addressed some concerns of mine. I will maintain the score", "rebuttal": "We sincerely appreciate your insightful feedback and constructive comments, which are invaluable in improving our work.\n\n**Response to Weakness1 & Question2:**   \nYou've rightly pointed out the importance of conditional generation for practical applications. We agree entirely. Our initial focus was to establish the model's fundamental generation capabilities. However, we've conducted preliminary experiments on conditional generation incorporating static features during this rebuttal period.\n\nSpecifically, we integrated age, gender, and admission diagnosis into the Temporal Modeling (Section 3.3). For training, we prepended these static features to the sequences, and the model learned the combined sequence auto-regressively. During generation, the model uses real static features as a condition to produce the complete sequence. To further explore conditional generation, we also experimented with providing initial medical context as a condition. This involved providing the model with real static features and approximately one-fourth of the initial real-data events from the sequence, with the model then generating the remaining three-fourths of the sequence.\n\nTo evaluate if the generated sequences effectively captured the relationship with static features, we used three classifiers to predict each static feature (age, gender, admission diagnosis) from the generated sequences, excluding the static feature portion itself. Age was binned for this classification. Model performance was evaluated using micro-AUROC for multiclass classifications (age and admission diagnosis) and AUROC for binary classification (gender).\n\n| Model                          | Age  | Gender | Admission Diagnosis |\n|--------------------------------|------|--------|---------------------|\n| Real                           | 0.72 | 0.67   | 0.89                |\n| RawMed (static only)           | 0.65 | 0.52   | 0.86                |\n| RawMed (static+1/4 initial events) | 0.69 | 0.59   | 0.88                |\n\nThese results show comparable performance for age and admission diagnoses between real and synthetic data, especially when leveraging the initial medical context. The increased AUROC values when conditioning on a portion of initial events (e.g., 0.69, 0.59, 0.88 for age, gender, and admission diagnosis, respectively, compared to 0.65, 0.52, 0.86 with static features alone) experimentally confirm our framework's flexibility in effectively supporting conditional generation using a richer set of features, leading to improved synthetic data quality. While we observed a slight drop in gender prediction compared to real data, we believe there's room for improvement with further methodological enhancements and hyperparameter tuning, given the short experimental timeframe. Due to these constraints, experiments were limited to the eICU dataset; we will update with MIMIC-IV results (for age, gender, and admission type) during the discussion period.\n\nRegarding the extension to a hybrid generation mode with user preferences, this is indeed a very interesting direction. It would be feasible to fine-tune RawMed using techniques such as Direct Preference Optimization (DPO) [1] with a separate dataset that contains both \"preferred synthetic data\" and \"un-preferred synthetic data.\" This aligns with our future research interests in developing more controllable and customizable synthetic data generation.\n\n\n---\n\n\n**Response to Weakness2:**  \nThis process is not heuristic but rather a rule-based approach that adheres to the minimum requirements of time-series relational tables, including consistent table naming, column naming, preservation of column-value pairs, and adherence to the temporal ordering of events. By predefining the table names, column names, and data types (e.g., numeric or categorical) for EHR datasets, Algorithm1 can be applied to diverse datasets without additional tuning or modifications. Indeed, the same code was successfully applied to the MIMIC-IV, eICU, and MIMIC-IV-ED datasets.\n\n---\n\n**Response to Question1:**  \nThe errors observed during the conversion of text to tabular format, as detailed in Algorithm 1, are primarily syntactic inconsistencies. These include incorrect table names, non-existent column-value pairs, mistyped column names, and mismatched data types. For example, a column that should be named \"value\" might appear as \"value value\", or \"originalrate\" might be truncated to \"original.\" Additionally, a numeric column might contain non-numeric values like \"1..23\" or \"8. 5. 6.\" instead of valid numerical entries. We posit that these errors originate from the auto-regressive modeling process within the compressed text space. These syntactic inconsistencies are not unique to RawMed. Other text-based autoregressive models for tabular data encounter similar errors. For example, the GReaT paper also discards syntactically invalid samples, highlighting a shared challenge in this domain.\n\n\n---\n\n\n**Response to Question3:**  \nWhen compressing raw EHR data with Residual Quantization (RQ), our method provides acceptable overall reconstruction accuracy on the test set. We achieved 99.8% token-level accuracy on eICU and 98.2% on MIMIC-IV, indicating that the data's general structure and core information are well-preserved across both datasets.\nSpecifically, for eICU, table name reconstruction accuracy was 99.9%, column names 99.9%, categorical column values 99.8%, and numerical column values an estimated 99.4%. For MIMIC-IV, table name reconstruction accuracy was 99.9%, column names 99.9%, categorical column values 99.8%, and numerical column values an estimated 91.2%. While the reconstructed data might not perfectly match the original, the preserved information typically remains adequate for most data utilization purposes.\n\n\n---\n\n**Reference**\n\n[1] Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al.)"}, {"review_id": "rr5c3Aab8L", "reviewer": "Reviewer_Uzbn", "summary": "This paper proposes RawMed, which recasts time-series electronic health records (EHRs) as a sequence of events, trains a Transformer to auto-regressively predict the next event in a sequence, and uses the pre-trained transformer to generate synthetic time-series EHRs. Although autoregressive transformers have previously been applied to generating tabular data and synthetic time-series EHRs at the temporal resolution of visits, this is the first time they have been applied to generating time-series EHRs at the original temporal resolution of the data. The raw data is prepared for the Transformer using a textual embedding, BERT-based tokenizer, and pre-trained encoder. After applying the reverse process to the output of the Transformer, the generated sequence of events is cleaned to ensure it corresponds to a feasible EHR, and finally the generated sequence of events is converted into a synthetic EHR. \n\nRawMed is evaluated using a dataset comprising three time-series tables from the MIMIC-IV and eICU datasets. Due to the lack of an existing benchmark for generating multi-table, time-series EHRs, generic multi-table generation methods such as RC-TGAN and ClavaDDPM are used as baselines, as well as RealTabFormer, a fine-tuned large language model. RawMed outperforms the baseline methods across a range of metrics designed to test the distributional similarity of the generated data and it's effectiveness as training data for downstream tasks.", "questions": "## Questions\n\n- What percentage of generated events were modified or rejected and what percentage of generated patients were rejected by the post-processing steps?\n\n- Where applicable, were the post-processing steps applied to the baseline models?\n\n- How was the $k$ in the top$-k$ sampling chosen? It feels like the distribution of the generated data depends hugely on this choice, but the impact of varying $k$ is never analysed.\n\n## Minor Comments\n\n- You should include at least a brief mention of HALO (see [here](https://www.nature.com/articles/s41467-023-41093-0.pdf)), which uses a Transformer architecture to generate synthetic EHRs with visit level temporal resolution.\n\n- On line 87 you use a double negative.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n- The paper proposes a unique approach for generating synthetic EHRs, the chosen datasets and baselines are suitable, and the set of evaluation metrics is extensive, although not necessarily complete (see weaknesses).\n- The proposed method achieves strong performance across the whole range of evaluation metrics.\n- The paper is well written.\n\n## Weaknesses\n\n- The paper repeatedly highlights the 'minimal preprocessing' of the data as a benefit of RawMed. I have two issues with this:\n  1. The authors never make it clear why preprocessing is a problem. In particular, they include one-hot encoding and normalisation as examples of the heavy preprocessing other methods require (lines 40 and 89), but these are lossless transformations. I believe the authors wish to make the point that preprocessing steps such as numerical binning and aggregation lose information and RawMed avoids operations like these. However, \n  2. RawMed uses extensive preprocessing to prepare the data for the Transformer, including a textual embedding, BERT-based tokenizer, and pre-trained encoder, the last of which loses information, as can be seen in the change of the patientweight distribution after reconstruction in Figure 2. Furthermore, the paper does not evaluate the accuracy of the reconstruction.\n\n- A possible disadvantage to using an auto-regressive next token predictor to generate synthetic EHRs is the model under-representing low probability events. The proposed suite of evaluation metrics does not test the generated data for this failure point.", "was_revised": true, "final_justification": "The authors have addressed my concerns regarding the separation between lossy and lossless preprocessing. Overall, I feel the paper makes a significant contribution to one sub-area of machine learning. I am therefore maintaining my score, and recommending the paper for acceptance.", "rebuttal": "**Response to W1-1:**  \nThanks for the opportunity to clarify our \"minimal preprocessing\" claim. Our intent is to highlight how RawMed significantly reduces reliance on lossy preprocessing steps that are prevalent in existing EHR synthesis methods.\n\nTo clarify what constitutes this \"lossy preprocessing\", we refer to operations that fundamentally distort or reduce the original data. This includes techniques like binning continuous values or aggregating data into fixed time intervals, which sacrifice the original data's granularity. Similarly, term normalization can eliminate subtle distinctions present in the raw information by grouping or standardizing terms. We agree that one-hot encoding is a lossless transformation and will clarify this distinction in our paper.\n\nCrucially, the first major limitation we pinpoint in existing works is their reliance on feature selection. This is a prime example of extensive, lossy preprocessing. It involves domain experts manually choosing only a subset of features they deem relevant for specific downstream tasks, thereby discarding vast amounts of original, potentially valuable information from the outset.\n\nThe core issue with all these lossy transformations—be it feature selection, binning, aggregation, or certain normalizations—is the inevitable reduction in data utility. For instance, data aggregated to hourly \"averages\" cannot accurately answer queries requiring precise \"counts\" within that hour, nor can it support analyses demanding finer temporal granularity. When the diverse future uses of synthetic data are unknown, losing original information inherently limits its overall versatility and applicability. RawMed directly addresses this by avoiding such transformations, thus preserving the richness of the raw data for broader utility.\n\n---\n\n**Response to W1-2:**  \n**RawMed's preprocessing is not extensive**. Unlike other methods requiring complex, domain-specific feature engineering, RawMed unifies all data into a text format. It then applies standard, general-purpose steps like text embedding and tokenization. This significantly reduces the overall preprocessing burden and complexity, making our approach comparatively minimal.\n\n**Furthermore, RawMed's preprocessing is near-lossless**. Tokenization and text embedding primarily change the data's representation, not its content. Any potential information loss can occur during the compression stage within our generative modeling part, not during the initial preprocessing. The encoder’s ability to preserve information is empirically validated by token-level reconstruction accuracy on the test set, reaching 98.2% on MIMIC-IV and 99.8% on eICU, which indicates minimal information loss.\n\nWe hope this response fully addresses your questions regarding our stance on preprocessing. We will also incorporate these clarifications and discussions into the introduction and Section 2.1 (Related Work) of our paper to provide a more comprehensive explanation.\n\n---\n\n**Response to W2:**  \nWe appreciate your valid concern regarding the potential for auto-regressive models to under-represent low-probability events, and the lack of a direct metric for this in our current evaluation suite. However, our metrics indirectly address this point. Specifically, our CDE (Column-wise Density Estimation) metric, when applied to item-related columns (e.g., specific lab tests, medications), measures the marginal distribution similarity across all possible events within that column. Additionally, our I-CDE (Item-wise CDE) metric extends this by averaging CDE across various item types, encompassing relatively rare drug classes and lab test varieties. In both CDE and I-CDE, RawMed consistently demonstrated superior fidelity to the real data compared to other baseline models. \n\n---\n\n**Response to Q1:**  \nOur post-processing pipeline involves two main stages: converting text to tabular format and enhancing data quality.\n\nIn the \"converting text to tabular format\" stage, we observed the following event-level modification and rejection rates: 0.42% modified and 0.07% rejected in eICU, and 0.73% modified and 0.17% rejected in MIMIC-IV. At the patient level, rejection rates were notably higher: 2.71% in eICU and 18.23% in MIMIC-IV.\n\nFor the \"enhancing data quality\" stage, stricter quality checks led to higher modification and rejection rates at the event level. In eICU, approximately 1.39% of generated events were modified and 0.49% were rejected. For MIMIC-IV, 1.07% of events were modified and 0.79% were rejected. These stringent checks resulted in significantly elevated patient-level rejection rates: 16.90% in eICU and 54.53% in MIMIC-IV.\n\nThe significantly higher patient-level rejection rates, compared to event-level rejections across both stages, are a direct result of our stringent data integrity policy. A single erroneous event leads to the rejection of an entire patient's data. For instance, MIMIC-IV patients can have up to 243 events, all of which must be accurate to ensure the patient's data is retained within the dataset.\n\n---\n\n**Response to Q2:**  \nWe appreciate you raising this point. We applied an equivalent level of post-processing to the RealTabFormer baseline in Table 5. However, extending this same strict post-processing to all baseline models, particularly those in Table 2, 3, and 4, turned out to be challenging and largely impractical. For example, applying our full post-processing pipeline to baseline models trained on MIMIC-IV resulted in approximately 99% of their generated data being discarded. This high rejection rate indicates that most baseline models couldn't perfectly generate all patient events under our strict post-processing standards.\n\nNevertheless, to ensure a fair comparison of model performance alone, we applied a less stringent post-processing to RawMed, resulting in an output quality comparable to the baselines in Tables 2, 3, and 4.\n\nThe resulting performance is detailed in Tables A, B and C, which can be directly compared with the results in Tables 2, 3, and 4 of our main paper. We could not include those results here due to space limitations.\n\nGenerally, the performance for most metrics remains similar to our prior results, where full post-processing was applied to RawMed. However, specifically for MIMIC-IV, our model's PCC and SMAPE values are slightly higher than our prior results and also higher than ClavaDDPM, indicating slightly worse performance in these metrics. Despite this, across all other metrics and consistently compared to other baselines, our model demonstrates superior performance.\n\nHowever, we emphasize that full post-processing is crucial for generating high-integrity data with RawMed. We do not recommend using partial post-processing, as it fundamentally compromises the quality of the synthetic data.\n\n\n**Table A:  Results of single-table evaluation on MIMIC-IV and eICU datasets.**  \n\n| Dataset | CDE   | I-CDE | PCC   | I-PCC | ER    | SMAPE  |\n|---------|------:|------:|------:|------:|------:|-------:|\n| mimiciv |  0.03 |  0.05 |  0.09 |  0.15 | 18.74 | 106.09 |\n| eicu    |  0.05 |  0.12 |  0.07 |  0.13 | 49.44 |  62.12 |\n\n**Table B: Results of clinical utility evaluation on MIMIC-IV and eICU datasets**  \n\n| Dataset | MEDS-TAB      | GenHPF      |\n|---------|---------------|-------------|\n| mimiciv | 0.87 ± 0.08 | 0.80 ± 0.10 |\n| eicu    | 0.83 ± 0.09 | 0.78 ± 0.10 |\n\n**Table C: Results of temporal fidelity evaluation on MIMIC-IV and eICU datasets**  \n\n| Dataset | Next Event Predict (F1) | Time Gap | Event Count |\n|---------|---------|---------:|------------:|\n| mimiciv | 0.15 ± 0.00 |   0.01 |        0.05 |\n| eicu    |    0.25 ± 0.03 |  0.02 |        0.06 |\n\n\n---\n\n**Response to Q3:**  \nThank you for your question about how we chose the k-value in top-k sampling and your valuable point about the lack of analysis on its varying impact on generated data. You're absolutely right that the choice of k significantly influences the data's characteristics.\n\nInitially, we conducted preliminary, small-scale experiments using the MIMIC-IV and eICU dataset, drawing inspiration from autoregressive sampling in NLP. We started with small k-values like 5, 10, and 15. However, we realized these values didn't accurately reflect the event distribution of the real data. This led us to intentionally increase k, arriving at our chosen values through a more heuristic approach rather than a full, systematic analysis at the start. While we didn't perform a complete, systematic analysis in the beginning, your insightful question prompted us to look closer at neighboring k-values.\n\nDue to time constraints, we've focused on two immediate surrounding values, but these preliminary results already highlight the impact on performance: For the eICU dataset, a k-value of 150 yielded the highest TSTR AUROC at 0.751, significantly outperforming k=100 (0.728) and k=200 (0.723). Similarly, for the MIMIC-IV dataset, a k-value of 250 demonstrated the best performance with a TSTR AUROC of 0.797, notably better than k=200 (0.773) and k=300 (0.776). We acknowledge the critical role of k and are committed to providing a more comprehensive analysis with a broader grid search during the discussion period, continually updating our findings to further solidify justification for these choices."}]

Author rebuttal:

Meta-review: This paper introduces RawMed, a framework for generating raw, multi-table, time-series EHRs with minimal preprocessing, alongside a comprehensive evaluation suite that spans fidelity, temporal dynamics, inter-table dependencies, downstream clinical utility, and privacy. The reviewers consistently highlight the novelty of the problem formulation and the breadth of the evaluation framework as the main strengths. The empirical validation on MIMIC-IV and eICU demonstrates convincing gains over adapted baselines, particularly in modeling temporal fidelity and preserving raw data structure.

Overall, while the technical novelty lies more in the integration of known components than in the invention of new generative methods, the framing of the problem, the evaluation toolkit, and the demonstrated empirical results represent a meaningful and timely contribution to synthetic healthcare data research. On balance, the strengths outweigh the weaknesses, and I recommend acceptance.
Decision: Accept (poster)