Title: Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Electronic Health Records (EHR)

EHR synthesis

time-series EHR

multi-table data
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)


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n## 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strength:
- 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.
- The paper introduces a well-rounded evaluation framework that thoroughly assesses synthetic data across multiple critical dimensions

Weakness:
- 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. 
- 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?

QUESTIONS:
See above,

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03

comments: 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.

STRENGTHS AND WEAKNESSES:
Strength:
1. 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).
2. The corresponding eval framework is novel covering the complex temporal and inter-table dynamics.
3. The experimental validation is thorough and convincing.

Weakness:
1. 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.
2. The handling of static patient attributes is a limitation, which doesnot explicitly model static features like gender or age.
3. Post-processing steps are still needed in the work to ensure the structural and temporal integrity of the generated data.

QUESTIONS:
refer to strength and weakness

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-05