GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care
Authors: Ruirui Wang
Yanke Li
Manuel Günther
Diego Paez-Granados
Venue: ICLR 2026 Poster (2026)
Keywords: irregular multivariate time series
graph neural network
deep learning for health
intensive care unit
explainability
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations. To reconcile auxiliary reconstruction and primary classification objectives, we developed an alternating decoupled optimization scheme that stabilizes training. On three ICU benchmarks (PhysioNet 2012 \& 2019, MIMIC-III), GARLIC sets the new state of the art in outcome prediction, significantly improving AUROC and AUPRC over best-performing baselines at comparable computational cost. Ablation studies confirm the contribution of each module, and feature-removal trials validate the fidelity of importance attribution through a monotonic performance drop (full > top 50\% > random 50\% > bottom 50\%). Real-time case studies demonstrate actionable risk warnings with transparent explanations, marking a significant advance toward accurate, explainable deep learning for irregularly sampled ICU time series data. Moreover, we demonstrated GARLIC's superiority in data imputation and classification on various time-series datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks.

Published as a conference paper at ICLR 2026
GARLIC: GRAPH ATTENTION-BASED RELATIONAL LEARNING OF MULTIVARIATE TIME SERIES IN INTEN-
SIVE CARE
Ruirui Wang"
Yanke Li SCAI Lab, D-HEST ETH Zürich SPF yanke.
Department of Informatics
University of Zürich
Manuel Günther
Diego Paez-Granados
Department of Informatics
SCAI Lab, D-HEST
University of Zürich quenther@iEf uzh. ch
ETH Zürich & SPF
diego. www.paaet.ethz..c..
ABSTRACT
Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross- dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations. To reconcile auxiliary reconstruction and primary classification objectives, we developed an alternating decoupled optimization scheme that stabilizes training. On three ICU benchmarks (PhysioNet 2012 & 2019, MIMIC-III, GARLIC sets the new state of the art in outcome prediction, significantly improving AUROC and AUPRC over best-performing baselines at comparable computational cost. Ablation studies confirm the contribution of cach module, and reature-removey trials validate the fidelity of importance attribution through a monotonic performance drop (full top 50% random 50% > bottom 50%). Real-time demonstrate actionable risk warnings with transparent explanations, marking a significant advance toward accurate, explainable deep learning for irregularly sampled ICU time series data. Moreover, we demonstrated GARLIC's superiority in data imputation and classification on various time-series datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks.
studies
INTRODUCTION
Continuous monitoring of critically ill patients in Intensive Care Units (ICU) generates vast streams of multivariate time series data, vital signs, laboratory results, and therapeutic interventions, which irregularly sampled and rife with missing values. Effectiie analysis of this data promises timely detection of deterioration and personalized treatment, yet two challenges impede progress. First, the inherent irregularity and heterogeneity of clinical measurements render standard sequence models (e. RNNs, Transformers) suboptimal: naïve imputation biases downstream tasks, while specialized approaches (e. g., GRU-D (Che et al. 2018). Latent-ODEs (Chen al. 2018). mTANND (Shukla & Marlin 2021) often ignore inter-sensor dependencies. Second, interpretability is paramount in healthcare, but prevalent post-hoc methods (e. g., Integrated Gradients (Duell et al. 2023). SHAP (Lundberg & Lee Lee]2017) (017)) demand extra computation and can yield inconsistent explanations, and even inherently interpretable architectures (e. g., RETAIN (Choi et al.) 2016), shapelet models (Li Wen et al. 2025 either sacrifice performance struggle with irregular sampling.
et all 202T
"Equal contribution

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In this work, we introduce Graph Attention-basee Relational Learning for Intensive Care (GARLIC), a unified framework displayed in figure I that simultaneously addresses irregular sampling, missing- ness, and the need for transparent decision making. GARLIC models each signals dynamics via decay-based latent feature encoder, constructs time-lagged summary graphs to capture inter-signal relationships on signal horizons, and refines representations through cross-dimeniiona sequential attention module. Critically, all attention weights and graph structures are learned end-to-end yield- ing built-in explanations at the level of individual time steps, particular sensors, and their interactions, without degrading classification accuracy or efficiency in predicting clinical outcomes.
Our contributions are threefold: Methodologically,, we propose a mullistage graph suuphatttentoon ar- chitecture that integrates (i) decay-based imputation for irregular missingness, (ii) oeff-attentive, time-lagged graph learning to exploit spatial dependencies, and (iii) dinensninnii attention to fuse signal embeddings over time. Our method also achieves real-time integrated interpretability. By compressing learned attention matrices and sparsifying summary graphs via L₁ regularization, GARLIC provides concise, quantitative importance scores for each observation, signal, and edge in the sensor graph, enabling correct contribution estimations for practitioner- frienddl explanations without external explainer. Empirically, GARLIC sets new state of the art, while maintaining computational cost on par with leading baselines on benchmark ICU datasets. Furthermore, we demonstrate GARLIC's superiority in data imputation and classification various datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks and datasets.
2 RELATED WORK
Irregular Multivariate Time Series Classification. To handle irregularly sampled multivariate time series, recent methods can be broadly categorized based on how they handle missingness and model temporal structure. Some approaches replace standard imputation with learnable interpola- tion mechanisms, such as exponential decay or decomposition- based interpolation, allowing more expressive modeling of temporal trends (Che et al. 2018 Chen et al., 2018 Shukla & Marlin 2019).
Others forgo explicit imputation by modeling continuous-tima latent dynamics using neural ODEs or stochastic processes, sometimes integrating them with attention mechanisms (Rubanova et al. 2019
Chen et al. 2018; Jin et al.] 2023 Chen et al. 2023 Park et al. 2025). Attention-baser architectures further exploit temporal information via time-aware encoding, adaptive aggregation over observed
values, or learnable gating schemes tailored to irregular inputs (Horn et al. 2020; Tipirneni & Reddy
2022 Shukla & Marlin 202I; Huang et al., 2024, Wang et al./ [002]] Zheng et al. [024] Zhang et al. 2023). Some methods transform time series into alternative structures, such as images or graphs, to leverage architectures such as vision transformers or pesssage-passingg networks (Li et al.,
2023] (Zhang et al. 2022). Inter-signal dependencies, though commonly used during classification, are rarely exploited during missingness modeling. For example, RAINDROP (Zhang et al. 2022) 2024
claims to learn such dependencies, yet both our results and the prior analysis (Zheng et al.
show that its best performance comes from fixed graphs, indicating a limited benefit from graph learning. In contrast, GARLIC explicitly leverages both temporal and inter-signal dependencies during missingness modeling, leading to more effective handling of irregular time series.
Explainable Models for Time Series. Explainable methods in time series analysis are broadly catego- rized into post-hoc explainers and self-interpretablle models (Zhao et al. 2023). Post-hoc approaches such brased attribution methods (Guo et al. 2019 Duell al. 2023), perturbation- based techniques (Kashiparekh et al., 2019). and surrogate modeling via LIME or SHAP (Ribeiro et al.) 2016; Lundberg & Lee 2017) aim generate explanations after model training, often through back- propagation or input masking. Although these methods are model-agnostic and widely applicable, they require additional computation and may yield inconsistent attributions across similar inputs. Self-interpretablee models aim to embed transparency directly into the training and inference process. Shapelet-based approaches (Li et al. 202T, Ma al., 2020b; Younis et al., 2024 Qu et al. 2024) Wen et all 2025) learn prototypical subsequences that act as
components for classification. Despite offering intuitive visualization, these methods are primarily validated on regularly sampled physiological data such EEG or ECG (Wen et al. 2025 Abdullah al. 2023 Neves et al. 2021 and their interpretability remains hard to quantify objectively. Attention based models provide more flexible explanations by interpreting learned attention weights or recalibration mechanisms (Choil et all, 2016, Guo et al., [2019] Qin et al. 2017 Ma et al., 2020a).. However, most existing methods assume regular sampling and fully observed inputs, limiting their reliability on irregularly sampled
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medical data, where unmodeled temporal gaps introduce explanation bias. These limitations mo- tivate the need for more interpretable methods that remain robust in real-world conditions. GARLIC addresses this need by incorporating interpretable modeling into the ceetinnnnss--aandiing process, enabling faithful explanations in the presence of irregularity and missingness.
3 PROPOSED METHOD
3.1 PROBLEM DEFINITION
We consider the task of predicting clinical outcomes from multivariate time series sampled irregularly in intensive care settings. Formally, let DD = ((Sn; } x=1 episodes, where Yn E (1...., C} is the outcome label for episode Sn, Each episode S, consists of asynchronous observations of K clinical signals (e.g., vital signs, laboratory tests), aligned to timeline of up to ''' discrete time steps. We represent each episode as an input matrix X E IR K'T where k stores the value of signal k time t, and a corresponding binary mask M E {0, 1] K indicating whether the value is observed. These signals exhibit heterogeneous statistical properties and are sampled at irregular intervals, leading to misaligned time axes and pervasive missingness. Our objective is to learn a model that (i) accurately predicts each from the irregular multivariate time series S, and (ii) provides transparent attributions at the observation-leva foster clinical trust.
be a labeled dataset of N patient
3.2 GARLIC
Figure I shows our proposed modular framework GARLIC for the prediction of clinical outcomes that combines local reconstruction with global reasoning. Specifically, graph-based message passing reconstructs missing features from local temporal and inter-signal context, while cross-dimensional sequential attention captures global dependencies across time and signals for accurate prediction. To align the auxiliary reconstruction task with the primary prediction objective and stabilize the training, we employ an alternating decoupled optimization strategy that explicitly decouples these two tasks.
3.2.1 LATENT FEATURE MODELING
Clinical time series are sparse, irregular, and heterogeneous, so naive imputation (e. g., mean or zero fill) distort signal-specific dynamics. We therefore introduce a latent feature modeling stage that combines time-aware imputation with per-signal encoding. We adopt an exponential-decay mechanism (Che al. (2088) model the diminishing relevance of past observations. Given the elapsed time A, since the last observation, the decay factor and the imputed value & at time t are defined as:
(1) where wi. and by are learnable parameters, Fv,t is the most recent observed value, and 7y. the empirical mean. We then construct an augmented input at each time step retain both the observed or imputed value and the missingness indicator mg,t E 13:
t = " + (1 (1-7))= 7x with
exp { max(0, A: + by)}
k = [ ma,4 + Ōk, mx,),,), ma,e"
(2)
Given the heterogeneity of clinical signals, we apply dedicated two-layer MIP each augmented input & yielding latent embeddings Z E D.
ZA. MLP (x..1)
(3)
This signal-specific encoding enables the model to capture the distinct statistical scale, dynamics, and semantics of each clinical signal, which would be obscured under a shared encoder.
3.2.2 TIME-LAGGED GRAPH MESSAGE PASSING
To reconstruct missing values in clinical time series, we introduce a local graph- based module that exploits the inherent locality of physiological signals, where each signal's value is strongly influenced by its recent temporal history and interactions with related signals. This temporal and inter-signal locality motivates design that restricts inference to short-range window, in contrast
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Dats Flow
Explenation Caltalaion Fiom
Loax Funcioon
Prwdiction
Goond muth
Bullideesa roen
Explrater Calokatoon
Origiaa
Deccay linps tettinn
Vinu rignnau
Cre
Allendon
Dacote
(6)
Modeling
(b) Time-Lagged Grach Message Passing
Gross-Cimensoonal
Figure Overview of GARLIC. In the first stage, irregularly-sampel inputs are imputed into signal-specific latent space. Then, inter-signal dependencies are modeled through learnable graph- based message passing system, enabling the reconstruction of the original observations. Attention through time and signals enables GRU model to learn dependencies medium time scale, to provide interpretable predictions.
to the global modeling used later for classification. Specifically, given latent embeddings 2t. extract a temporal window of length 7 + for each signal. where т controls the size of the local context, add sinusoidal positional embeddings PE(j), and apply signal-wise lag attention:
ē = LagAttig (IZR. + PEO) Σ
5=== BudieVku
exp (g.ckk.//DD
(4)
Bk.jt
Kr.tku.' /VD)
Ej==-, exp
with Vkj W.(ze, + PEG)). kEj Wa(Ze, + PE(j)). Du.t = W + PE(()) and e Σ Pj=t-r Vk.j. is the window-level representation for signal & at time t, with B being the corresponding attention weight. We stack the outputs for K signals form E, E RxD which summarizes contextual information on the temporal window for each signal.
To model interactions among signals given temporal lag т, we construct a summary graph G, defined by a learnable adjacency matrix W, E k where each node represents signal and edges encode pairwise dependencies. Graph-based message passing is then applied to propagate information across correlated signals:
= - E, E EKXD,
(5)
where H, denotes the time-f representations of all signals, updated based on historical context and relational dependencies from the graph Gr- By decoupling attention-base temporal encoding from graph propagation, this module flexibly captures short-range dynamics and inter-signal dependencies. Moreover, varying the lag t enables adaptive multi-scall modeling of physiological dependencies. We treat as hyperparameter, with ablation results in Appendix D.2
3.2.3 CROSS-DIMENSIONSS SEQUENTIAL ATTENTION
To construct global representation for classification, apply two-stage attention cascade the signal-wise encoding (H,) produced by the message passing module. At each time step f, we first apply attention mechanism SignalAttn over the K signals to obtain weighted aggregation:
K
exp ((g)) (L./vDD
ú, SitnnaAAtt(HH.)
sig Ogg
Uuit
sig O'g.t
(6)
Σ 1Ø=1
exp ((q)) K K k///VD)
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sig with ueee = h,,, Kg, Wi of WW while dget are the learned attention weights and i, summarizes the cross-signal information at time f. To capture temporal dependencies, the sequence {ūr] is processed by a Gated Recurrent Unit (GRU):
g: GRU(U, gi-1),
(7)
which models local temporal continuity, reflecting gradual transitions in clinical states. To com- plement the GRU's locality bias and enable direct modeling of long-range dependencies, we apply temporal self-attention over the GRU outputs, incorporating sinusoidal positional encoding PF((t):
Y TemporalAttu(Igr + +E(()) E ER
{
T
time &umeyy ap,t Yu E=T
J
Y=
exp (d K//VDD
(8)
time HE
Σ exp (ql)Tk//VD)
with ye = (g + PE(()), K2 WE PE(())) W + PE(()) being the value, key, and query embeddings at time t', and ag is the corresponding attention weight. Finally, we average-pool Y over time and pass the result through a classification head and softmax to obtain class predictions ğ E RRC for all C' classes. This attention-based pipeline captures both signal-level importance and temporal saliency, supporting accurate downstream classification.
Comparison to Prior Work. Our design inspired by interpretable time-series models such as RETAIN (Choi et al., (016) and IMV-LSTM (Guo et al., 2019). which combine recurrence with attention to capture temporal dynamics and signal relevance. RETAIN applies attention at both the timestep and signal levels but aggregates signal contributions uniformly across time, limiting its ability to localize transient patterns. IMV-LSTM applies signal-level attention to the hidden states produced by per-signal LSTMs, which aggregate temporal information before computing importance, potentially obscuring instantaneous signal saliency. In contrast, GARLIC applies signal- level attention directly at each time step before any recurrent modeling, ensuring that the importance of fine-grained signals is preserved and explicitly captured prior to temporal fusion.
3.3 INTERPRETABILITE AND ATTRIBUTION COMPUTATION
Interpretability quantifies how transparently models decisions can be understood by humans (Zhang et all (22TT). GARLIC provides local interpretability by attributing each prediction directly to its input features. We compute input-level attribution scores by tracing learned importance weights backward from the model output. This backward trace mirrors the forward path through the model's three core components as illustrated in figure and described section 3.2
Attention-based saliency (section 3.2.3 ; Let sig 1K T denote the signal-level attention scores (a
of equation 6, and {a (T_ = the temporal attention scores of equation g We define the joint saliency as the element-wise product:
time Ouge,t O'
sig
8k,
which yields a relevance matrix S BR RKX((T-- (1++) capturing the importance of each signal-time pair.
Graph-based propagation (section 3.2.2
We propagate the relevance scores S across signals using
the transposed time-lagged graph W RR
k from equation 5 and redistribute them temporally 1K T'
using the attention weights {B k== -= 1_0 from equation 4 The resulting importance scores ax.t denote the propagated relevance of signal at time and are calculated as:
a = -, (WT
keety" ), (m.04..-)]-
(10)
Observation masking and redistribution (section 3.2. To isolate contributions of each obser-
vation, we apply the binary mask M E {0, 1 Since decay-based imputation blends multiple past inputs, attribution to unobserved entries is ill-defined. We approximate it by redistributing their contribution uniformly across observed positions of the same signal:
Okht magt + (1 mg.e) 21= OR (1-MAR) mp,))
final 0ggt
(11)
EP= mA, +e
5

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where 0 ensures numerical stability. The final attribution map A Afnall E ER assigns salience to individual observations a way that fully consistent with the models forward computation.
3.4
TRAINING STRATEGY
3.4.1 LOSS FUNCTION
During training, we make use of both an auto-encoder to learn representative features and impute reasonable values, and the classification head to predict clinical outcomes such as mortality or sepsis onset. Our training objective jointly enforces (i) accurate data reconstruction, (ii) a sparse, inter- pretable graph, and (iii) strong downstream classification performance, via the following components:
(i) Reconstruction Loss (Crec). After message passing, the fused hidden states are passed through decoder to reconstruct the input data. Let &t., denote the reconstructed values, 2 the observed data, and ma,t {0, 1] the binary mask indicating observed entries. The reconstruction objective minimizes the masked mean squared error.
(ii) Graph Regularization Loss gggraph) To capture inter-signal dependencies, the model learns a lag-r dependency graph G, with adjacency matrix W, E R where each entry denotes the influence between signals. To ensure that the learned graph highlights only the most salient dependencies, we apply an & regularization promote sparsity. This encourages the model focus on small set of essential edges, thereby improving interpretability and mitigating overfitting from spurious or redundant connections.
(iii) Classification Loss (Les). For prediction, we use categorical cross-entropy between the model's output ý E RC and the ground- -ounn label E [1,...,C), C).
Total Loss. We combine the above terms into single objective:
Σ k.t my, ( 2x,e)² +Ag | log By
(12)
Eeraph
Cas
where Ag and Ae balance the regularization and supervision terms. This multi-objective formula-
tion enables the model to exploit structural priors and label supervision jointly, improving both reconstruction fidelity and predictive performance.
3.4.2 ALTERNATING DECOUPLED OPTIMIZATION
Joint optimization of reconstruction and classification often leads to gradient interference due to their differing objectives: recon- struction seeks faithful input recovery, while classification prior- itizes discriminative features. To mitigate this conflict, we adopt alternating decoupled optimization strategy, inspired by De- FRCN (Qiao et al.) 2021). to isolate learning dynamics between modules. As illustrated in figure 2 our model consists of three parametrized components 0g, 0,, and 0g corresponding to: (a)
Prozen Component
> Formart Flaw
Naisablle Compatent
Gradlont
Eroe Eyept
Reconstruttoon Stage
Latent Feature Modeling (section 3.2. 1), (b) Time-Lagged Graph Message Passing (section 3.2.2). and (c) Cross Cross-Dimensional Stage 1, we up- date shared modules a) via composite loss of reconstruction, graph regularization, and classification, while fixing the classifier (0.):
Sequential Attention classifer (section 3.2.3
Can
6.
0ab f- 0u,b Vo,, (Eres + Xg Aggeraphh + dcLaxs)
(13)
(2) Classification Stage
In Stage 2, we freeze Oo,b and update 6, using the classification loss:
Figure 2: Alternating decoupled optimization training.
0, 2- 0. 17 Cols-
(14)
This decoupling reduces representational interference, stabilizes training, and improves classification performance. Full details and reasoning provided in Appendix A.3

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate our method on three widely used clinical time series benchmarks: MIMIC-III (Johnson al. 2016), PhysioNet Challenge 2012 (P12) (Silva et al. 2012), and PhysioNet Challenge 2019 (P19) (Reyna et al. 2019). These datasets feature diverse patient populations, varying recording frequencies, and significant missingness, reflecting the challenges of real-world ICU records. All tasks are framed as binary classifications: in-hospital mortality for MIMIC-III and P12, and sepsis onset for P19. Detailed preprocessing procedures and statistics are provided in Appendix B
Baselines. We compare method against strong baselines across two categories. Irregularity- aware models encompass: variants of RNNs (RNN-Mean, RNN-Decay, and RNN-A, details in Appendix C.T. GRU-D (Che et al.] 22018) ODE-RNN
(Rubanova et al. 2019) L-ODE-RNN (Chen
atal_ 2018), L-ODE-DDE (Chen et 2018), mTANND (hukka & Marlin (021). Warpformer (Zhang
al. 2023), and MTSForrmrr (Zheng et al. 20244, which explicitly model missing values, irregular sampling, or continuous- time dynamiis. This category also includes MTGNN (Wu et al., (2020) and Raindrop (Zhang al. 2022), which further incorporate inter-signal dependencies through learned or predefined graphs. Interpretable models are: RETAIN (Choi et al. (016), IMV-LSTM (Guo et al. 2019), and DARNN (Qin et al. 2017), which aim to provide signal- and temporal-lewe interpretability. Implementation details and hyperparameter settings for all baselines are given in Appendix C.1
Implementation Details. Each dataset is split into training, validation, and test sets an 8:1:1 ratio, with stratified sampling on both outcome labels and key patient characteristics (e. g., gender, ICU type) to preserve class balance. Hyperparameters are chosen via grid search on the validation split, and for baselines, mirror the tuning ranges and protocols of their sources to ensure fair comparison; detailed settings are provided in Appendix C.T Models are trained for up to 100 epochs with Adam optimizer (learning rate tuned per dataset), employing early stopping with patience of 10 epochs based on the validation loss. We report both AUROC and AUPRC to account for class imbalance, and all results averaged over five trials with distinet random seeds. All experiments were conducted on server with an Intel Xeon Silver 4314 processor (16 cores, 2.40GHz), 256GB RAM, and dual NVIDIA GeForce RTX 3090 GPUs (24GB VRAM each). All training experiments utilize a single GPU configuration.
Table 1: Model performance in terms of AUROC and AUPRC. For both metrics, report mean + std in % over five random seeds. The first block lists strong baselines designed for irregular time series, the second block includes interpretable models, and the final block shows results for proposed method. We highlight the best and the second-best results per column.
P12
P19
MIMIC-III
Models
AUROC
AUPRC
AUROC
AUPRC
AUROC
AUPRC
RNN-A:
1..4
47.29+4.33
89.21
51.20+2.50
82.98+2.27
50.31+4.222
RNN-Mcan McMan
82.38+2.11
47.80
88,08
45.80=22.10
84.18+1.70
50.06:5.117
RNN-Decay
84.26+11.10
49.81+3.31
89.15=1.115
49.73=#3446
87.634:0.9
56.2913.229
GRU-D
84.45+1..00
50.74-14.96
88.73-10.86
47.81+3.37
86.36.+1.68
16.14
ODE-RNN
83.02+11.72 48.64412.68
89.97+0.86
54.29++2.76
88.07-40.77
61.03++2.00
L-ODE-RNN
80,59+2.06
41.40-44.15
89.63++1.31
51.02+33.77
86.98--0.58
56.88++2200
L-ODE-ODE
83.93+1.14 1..55
90.0112.04
53.5416.12
87.3610.94
59.09
MTGNN
80.9511..71 43.61114.93
86.9411..65
40.97=5.26
86.24410.67
51.74110.92
mTANN
84.30.11.69
50.05
81.73
37.27+3.993
88.0011..1
57.7342.21
RAINDROP
83.03+1.377
45.91+3..55
87.41+1.13
46.33+3.09
87.18+1.51
57.06+66.01
Warpformer
84.88+00.48
50.62+11.25
89.95+0.557
54.10+2.33
89.17+0.55
61,52+ .93
MTSFormer
83.65+1.770
50.31++..30
87.88+0.61
48.80+22997
88.14++0.50
61.09+00.95
IMV-LSTM
84.02.11.30
1..08
84.80.12.76
42.87.18.27
86.85.11.33
54.96.13.61
DARNN
79.84.11.36
42.70.12.09
73.36.13.50
2..79
82.56.11.25
46.33.14.08
RETAIN
83.08
49.2743.01
78.09+2.22
26.04=1.35
82.40:+0.94
46.55
GARLIC
86.40
56.89
90.96-10.84
55.29-1
90.09-10.45
64.85 1.68

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4.2
MAIN RESULTS
Table I reports the AUROC and AUPRC of all methods on three benchmark datasets. GARLIC consistently achieves the highest scores, setting the new state-of-the-art performance. The improve- ment is especially notable on the P12 dataset, which contains substantial missingness (statistics in Appendix B). demonstrating the models robustness to sparse and irregular multivariate time series, Compared to other self-interpretable models with attention pechhnnims--suee as RETAIN, DARNN, and IMV-LSTM- GARLIC achieves substantially higher predictive accuracy while preserving interpretability.. Compared to irregularity models that primarily address temporal gaps and graph-based models like MTGNN and Raindrop that rely on static graphs without temporal modeling, GARLIC achieves better performance consistently by dynamically capturing time-lagged depen- dencies across signals. Apart from ICU outcome prediction, we also prove GARLIC's capability in data imputation and human activity recognition, showcasing its generalizability and applicability to broader tasks and datasets. More details can be found in Appendix F
Ablation Study. of ablations on P12 and P19 benchmarks, with results summarized in Appendix D.I demonstrate that each module substantially contributes to GARLICs overall superionty.
To assess the contribution of each component in GARLIC, perform series
[ These results
Model Efficiency. To evaluate the efficiency of GARLIC, we measure its training time and memory footprint on the P12, P19 and MIMIC III datasets, comparing against 15 baseline models, each configured with optimal hyperparameters and trained using the same batch size for fairness (more details including these quantitative comparative results and theoret- ical analysis are provided in Appendix (A.2). As an example shown in figure 3 GARLIC achieves the best performance while maintaining a comparable computational cost on P12 classification task.
Training Time AUROC (with Memory Fontprint)
GARLIC
LOC2 O2E
Rrlferwwn
Graph Learning. To better understand how our model cap- tures inter-signal dependencies during local reconstruction, we analyze the learned time-lagged summary graph Gr, parame- terized by the adjacency matrix Wr. Preliminary evaluations using an ensemble of advanced Al proxies indicate that the learned graph structures align well with clinical intuition. How- ever, rigorous validation by human medical experts remains an important direction for future work. Further details and discussions are provided in Appendix
O
92
18 Trahiin lafter)
Figure 3: Comparison of AUROC versus training time for various methods on the P12 dataset.
4.3 INTERPRETABILIT EVALUATION
To quantitatively evaluate the reliability of our models attribution scores, adopt perturbation based evaluation strategy inspired by the Remove and Retrain (ROAR) framework (Hooker et al., 2019 Guo et al. 2019; Meng al., (002)). Specifically, we compare model performance under three masking conditions against the original full-input setting: (i) Top 50 %0: For each sample, we retain the top 50% most important signal-time pairs as determined by attribution scores, masking the remaining inputs. If performance remains close to that of the full-input model, it suggests that the retained inputs capture the primary predictive signal. (ii) Bottom 50%: In this condition, we mask the top 50% most important inputs, retaining only the least important half. A marked drop in performance would indicate that the attribution scores correctly identify the most predictive features. (iii) Random 50%: : As control, we randomly mask 50% of the inputs, independent of attribution scores. This baseline allows us to disentangle the effect of informed attribution- based masking from random perturbations. In all settings, models are retrained from scratch the perturbed datasets.
In table 2 we report AUROC and AUPRC (mean + std over five seeds) as evaluation metrics. To assess statistical significance, we employ two complementary tests. First, we use the Two One-Sided Tests (TOST) procedure (Schuirmann, 1987) to test whether the performance under the top-50% retention condition is statistically equivalent to the full-input model, with equivalence margins of +5% for AUROC and +10% for AUPRC. Secondly, we apply Pages L test (Page) 1963) to examine whether performance follows the expected monotonic ranking: Full > Top-50% > Random-50%
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Table Interpretability assessment via input removal. AUROC and AUPRC (%, mean + std over five seeds) are reported for models trained on the full input and three 50% subsets of the data (top. random, bottom). Two One-Sided Tests (TOST) p-values evaluate equivalence between full and Top 50% models (margins =5% AUROC, 10% AUPRC). Pages L test p-values assess the monotonic ordering (All Top 50% > Random 50% Bottom 50%). *p <.05; **p <.005.
P12
P19
Models
AUROC
AUPRC
AUROC
AUPRC
84.02.11.30
49.08.13.28
84.80.1..76
42.87-1.277
IMV-1. AISSSTN
Top 50%
76.23+0.32
34,46+1.78
82.07+1.677
36.05+2.91
Random 50%
75.23+5.119
37.03+8.91
76.28+3.73
23.31+3.22
Bottom 50%
70.12+3.12
26.83+2.14
79.61+0.88
28,59+1.75
Equivalence (TOST p)
0.9944
0.9766
0.1024
0.0928
Monotone (Page's 's L Test p))
0.0011 **
0.0084
0.0032 **
0,0019 #s
1.38
42.70::2.09
74.36:3.500
20,89+12.73
Top 50%
73.92=t10.30
35.3811.83
72.17+4.27
17. 17.14+3.45
DARNN
Random 50%
65.27-+3.23
27.25++11.03
69.86-+4.49
17.52++4.21
Bottom 50%
67.17+8.38
27.05+66.9
70.34+66.15
15.40+
Equivalence (TOST p)
0.5661
0.3099
0.1697
0.0004 **
Monotone (Page's 's L Test p)
0.0301
0.0132
0,0440 *
0.0132 *
83.08
49,27:1.01
78.09112.22
26.04:11.35
RETAIN
Top 50%
77.04+1.08
37.18+3.15
74.74+1.64
1..17+1.84
Random 50%
72.89+5.51
34.11+7.87
69.08
17.17+3.009
Bottom 50%
76.23+11220
36,30+3.66
73.28+0.51
18.60 2.660+2.36
Equivalence (TOST p)
0.8814
0.8172
0.1346
0.0039
Monotone (Page's Test p)
0.0132
0.0132
0.0032 **
0.0053 *
8..40:10.66
56.89:1..5
90.96:10.88 84
25.29:+2.445
GARLIC
Top 50%
85.60=11.4 .48
52,9943.225
87.97
48.244 ,44
Random 50%
74.73-16.05
35.51
84.73:12.46
40.77-+6.11
Bottom 50%
71.09-+1.17
33.52-43.42
82.52++1.27
33.39-+-.47
Equivalence (TOST p)
0.0017 **
0.0079
0.0156 *
0.0399 *
Monotone (Page's L Test p)
0.0002 **
0.0004 **
0.0002 **
0,0002 **
Bottom-50%% Together, these tests quantify both the sufficiency of top-ranked features and the consistency of performance degradation under progressively less informative input subsets. Technical details of both statistical procedures are provided in Appendix G Across both datasets and metries, our model passes all eight statistical tests, with all Pages L p-values below 0.005, indicating robust and consistent interpretability. In contrast, three baseline models pass only four or five tests, showing weaker or less consistent attribution quality under identical conditions.
4.4 CASE STUDIES
To illustrate GARLIC's interpretability on real ICU trajectories, we analyze two case studies from the P12 and P19 datasets involving standard hemodynamic and metabolic variables (see Appendix [H.T). Key indicators include Mean Arterial Pressure (MAP), where values < 65 mmHg imply organ dysfunction, and White Blood Cell (WBC) count, signaling immune activation (Singer et al.) 2016).
Mortality prediction (figure 4a For non-surviving patient, GARLIC appropriately assigns only moderate importance to early, transient heart rate increases. As the episode progresses, the model identifies simultaneous oscillations across multiple blood pressure channels (MAP, systolic, and diastolic) as the primary risk drivers. This specific attribution to synchronous hemodynamic instability aligns with established indicators of clinical deterioration (Silva et al. 2012 Singer al. 2016). demonstrating the model's 's sensitivity to complex, multivariate risk factors.
Sepsis detection (figure 4b). In patient developing sepsis, the model successfully filters out early, isolated fluctuations. After hour 12, attribution peaks coincide precisely with synchronized multi-variable shift: decreasing MAP and bicarbonate (indicating metabolic acidosis), rising HR,

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Published as a conference paper at ICLR 2026
NBC Jount "S
18 1R.
DUN (ngrA)
Holasidd
(rening)
Nyyllt yedts
HR Upeet
Praditc Hors incuraily
Moddld Pobellly
Time (ninuts)
Time (baun)
(a) Case study for P12
(b) Case study for P19
Figure 4: Case Study Visualizations. aa A non-survivor: heart rate (HR), mean arterial pressure (NIMAP), diastolic pressure (NI) and systolic pressure (NISysABP) time series with corresponding normalized attributions and the predicted mortality probability over time upon available measurements, (b) A sepsis patient: leukocyte count (WBC), creatinine, bicarbonate (HCO,), blood urea nitrogen (BUN), mean arterial pressure (MAP), and HR time series with their attributions and the predicted sepsis probability over time upon available measurements. Importance scores are normalized within each signal to highlight temporal dynamics.
and elevating kidney function markers (creatinine, BUN). These patterns mirror standard diagnostic criteria for evolving sepsis (Singer et al. 2016 Reyna et al. 2019).
Overall, GARLIC generates plausible attribution maps by highlighting time steps where multiple physiological abnormalities converge, rather than overreacting to isolated noise. Additional case studies are detailed in Appendix
CONCLUSION
We introduced GARLIC, a unified framework for ICU time series that integrates (I) exponential- decay imputation encoding, (2) time-lagged graph message passing, and (3) cross dinns-ddmeniinal sequential attention, trained via alternating decoupled optimization to jointly reconstruct inputs and predict outcomes. On PhysioNet 2012 & 2019 and III, GARLIC achieves the state-of-the-an AUROC and AUPRC. Interpretability evaluation and case study analyses demonstrate that the model produces clinically meaningful and interpretable attributions. Additionally, GARLIC achieved competitive performance in data imputation and classification on various datasets beyond the ICU domain, showing its generalizability and applicability to broader time-series tasks (c.g.. human activity classification and imputation). This work advances disease-risk prediction by combining high accuracy with transparent, clinician-friendl. explanations, empowering healthcare teams to make more informed decisions and fostering trust in Al-driven insights.
I Limitations and future work. While GARLIC excels at binary outcome prediction with built-in explanations, several limitations remain that restrict its broader applicability. First, the model has not yet been evaluated for forecasting tasks, which important in many clinical applications. Next, the reliance on fixed-size sliding windows and discrete time lags may limit its flexibility in modeling irregular or continuous-tin and event-driven formulations. In addition, GARLIC currently excludes static patient features (e. g., demographics, comorbidities) and does not address the severe imbalance outcomes. We aim to incorporate such features and adopt strategies such as class weighting resampling. Finally, GA GARLIC does not yet support streaming or arbitrarily long ICU stays, and real-world utility hinges on integration with clinician feedback for iterative refinement.
signals; we plan to explore adaptive, window-free variants
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REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we provide data description and implementation details in section 4. including choice of model hyperparameters in Appendix C.2 can be accessed via
The implementation codes of GARLIC
ACKNOWLEDGMENT
This research project was partially supported by the Schweizer Paraplegiker Stiftung and the ETH Zürich Foundation (2021-HS-348) and the JST Moonshot R&D Program, Grant Number JPMJMS2034-18.
ETHICS STATEMENT
This research did not involve human subjects and, therefore, did not require Institutional Review Board (IRB) approval.
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A
ADDITIONAL DETAILS OF THE PROPOSED METHOD
A.1 ARCHITEECTRR DETAILS
A.1.1 LATENT FEATURE MODELING
Time-aware imputation. The imputation module uses an exponential decay mechanism with per-variable parameters Wi and by, initialized 0.1 and 0, respectively.
Signal-specific encoders. Each input variable is processed by dedicated Multi-LLayer Perceptron (MLP), implemented independently for each dimension. Each MLP has two fully connected layers with ReLU activation, both with output dimension Dr, which is shared tunable hyperparameter.
A.1.2 TIME-LAGGED GRAPH MESSAGE PASSING
Temporal context encoding. For each variable, we extract window size T+1 and apply sinusoidal positional encodings before computing attention across the window. This yields a temporally contextualized embedding per variable.
Graph-based signal interaction. A time-lagged graph G, is defined by a learnable adjacency matrix W, E R initialized the sum of an identity matrix and fully connected matrix scaled by small positive constant. This stabilizes training and introduces a weak prior toward dense connectivity. Message passing is performed via matrix multiplication with W--
Lag configuration. A single lag parameter is shared across variables. Future work may explore variable-specific lags for more flexible modeling.
A.1.3 CROSS DOISSSDDEENSONNLL SEQUENTIAL ATTENTION
Temporal modeling and fusion. Signal-level summaries are passed through GRU with hidden size Dr, followed by temporal self-attention over the GRU outputs. Positional encodings are added before attention, and the attended sequence is mean-pooled to obtain a global representation.
Classifier. The pooled vector is processed by a two-layer MLP with ReLU activation and hidden dimension Dr. producing the final prediction logits.
A.1.4 TRAINING STRATEGY
Loss function. To guide representation learning, the fused representations H are decoded back to the input space through two-layer MLP with ReLU activation and hidden size D,. Reconstruction supervised using masked mean squared error over observed entries. An & penalty W, encourages sparse graph structure, and binary cross-entropy is used for classification. The total loss is a weighted sum of reconstruction loss, graph sparsity regularization, and classification loss, where the latter two are scaled by coefficients Ag and Ae, respectively.
Alternating decoupled optimization. Parameters divided into shared backbone (imputation, encoding, time-lagged attention, message passing) and a prediction head (signal-level attention, GRU, temporal attention, classifier). Each epoch updates only one group while freezing the other. The two groups use separate learning rates and weight decay. Phase transitions are triggered by early stopping with patience, and each phase resumne from the best checkpoint of the previous one.
A.2 MODEL EFFICIENCY
A.2.1 THEORETICAL ANALYSIS
We analyze the computational and memory complexity of our model with respect to the sequence length and the number of variables K. The model employs Aliding-window mechanism, where each window spans +1 time steps, yielding the input feature dimension and D,, the hidden state size. Positional encodings are implemented via table lookups and incur negligible computational overhead.
T valid windows per sequence. Let Dy denote
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Table Training time (s/iter) and memory footprint (MB) of baseline models across datasets (batch sizes: P12-64, P19-64, MIMIC-III-1).
Method
P12 Time
P12 Mem
P19 Time
P19 Mem MIMIC-III Time
MIMIC-III Mem
GRU-D
0.946
152
0.132
1.948
RNN-mean
0.581
120
0.078
25
1.198
88
RNN-decay
0.475
140
0.159
27
0.600
87
RNN-delT
0.469
120
0.050
24
0,980
76
ODE-RNN
1.717
184
0.246
2.737
L-ODE-RNN
0.599
218
0.087
37
0.650
126
L-ODE-ODE
1.846
220
0.318
38
3.873
136
mTAND
0.072
2856
0.063
1007
0.205
6273
MTSFormer
0.148
2798
0.131
1653
0.107
1176
MTGNN
0.256
3864
0.185
2574
0.290
2151
Raindrop
0.242
1806
0.163
313
0.128
2431
WarpFormer
0.361
8566
0.164
3981
0OM
OOM
RETAIN
0.225
430
0.035
59
0.353
213
DARNN
0.597
2034
0.135
0.601
612
IMV-LSTM
0.998
4550
0,213
1977
5.284
7509
GARLIC
0.581
3322
0.184
1250
0.619
1128
Table 4: Lightweight GARLIC configurations on P19: varying time lag (feature_dim=33) hid-
den S1n_size=512)
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
32
512
8
90.96
0.184
1250
32
512
7
90.07
0.164
1060
32
512
6
89.51
0.153
1098
32
512
5
89.47
0.152
960
32
512
4
88.97
0.155
832
32
512
3
89.30
0.137
694
32
512
2
88.52
0.143
662
32
512
88.99
0.128
660
The per-layer computational complexity is detailed as follows: the signal-wise encoders require O?)KD;); the signal-wise time-lagged attention operates at O(TKDy) under the assumption that is small constant; message passing and signal-level attention introduce O(TK2 D,) complexity due to all-pairs interactions among variables; the GRU contributes OTTD); the temporal attention layer incurs ( D;,); and the final classification head adds O(D)).
Aggregating the above components, the overall computational complexity of the model is: O(TKD)) + O(TK²D,) O(TD)) O(T² O(D)). Neglecting the feature and hidden dimensions, the model exhibits an overall complexity of O(TK + TK² +T T2))
A.2.2 EMPIRICAL RESULTS
We benchmark the computational efficiency of GARLIC against fifteen baseline models terms of training time and memory usage. All models trained under identical batch sizes with their best hyperparameters to ensure fairness. The results, summarized in table show that GARLIC achieves comparable efficiency while maintaining strong predictive performance.
A.2.3 PERFORMANCE PERFORMANCE-EEFICIINC TRADE-OFF ANALYSIS
We evaluate the impact of model configurations computational efficiency, focusing on the time lag (r), feature dimension, and hidden size. The results, presented Tables 4 5 and 9 show that reducing these parameters leads noticeable decrease in training time and memory usage, although some minor drop AUROC scores is observed. Smaller configurations, achieved through shorter time lags reduced feature and hidden dimensions, lower per-iteration runtime and memory
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Table 5: Lightweight GARLIC configurations on P19:
varying hidden size (feature dim=32,
time_lag=8" lag=8)
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
32
512
8
90.96
0.184
1250
32
256
8
90.47
0.135
1186
32
128
8
90.52
0.134
1080
32
64
8
89.62
0.133
1150
32
32
8
88.53
0.133
1140
Table 6: Lightweight GARLIC configurations P19: varying feature dimension (hidden size=512.
(ime_la==8)
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
32
512
8
90.96
0.184
1250
512
8
90.46
0.136
710
8
512
8
89.98
0.126
566
footprint while incurring only small reduction predictive performance. These experiments indicate that GARLIC offers flexible trade-offs between efficiency and accuracy, achieving practical computational costs across wide range of configurations.
A.3 DECOUPLED OPTIMIZATION RATIONALE
We partition model parameters into three groups for decoupled optimization. The first group, 6a. includes the time-aware imputation parameters (wk, by) and the signal-specific MLP encoders, corresponding to the Latent Feature Modeling module. The second group, Ob, comprises the time- lagged attention module with positional encoding and the message passing layer with the learnable adjacency matrix, forming the Time-Lagged Graph Message Passing module. The third group. 8c. consists of the signal-level attention, GRU, temporal self-attention, and the final two-layer MLP Sequential Attention module.
classifier, which together compose the
Although reconstruction introduced as an auxiliary objective to improve representation quality by fully leveraging partially observed data, it is not fully aligned with the final classification task. The reconstruction components. e., (a) Latent Feature Modeling and (b) Time-Lagged Graph Message Passing are designed to faithfully recover the input signals, preserving temporal continuity and inter-signal structure. These modules prioritize fidelity to observed data, independent of its discriminative utility. In contrast, the classification module, e.. (c) Cross Pross-Dimensional Sequential Attention focuses on extracting task-relevant features that effectively differentiate clinical trajectories associated with distinct outcome classes. As result, it may intentionally discard input patterns that are non- non-predictive or noisy. This divergence learning objectives introduces representational interference when the system is trained end-to-end, causing unstable updates and suboptimal generalization.
To resolve this issue, adopt alternating decoupled optimization strategy, an approach generally applicable to multi-objective learning when components serve partially conflicting purposes. Al- though our overall task is classification, a strong auxiliary reconstruction loss introduces competing gradient signals that, if not properly managed, can impair model performance.
Our strategy alternates between two decoupled stages of optimization. In the first stage, we fix the classifier 6, and update the shared modules 0,,b by minimizing composite loss that combines reconstruction, graph regularization, and classification objectives:
0ab +- 0ab Ve (Crce + Ag + Xchets) with 0g fixed.
In this step, although 6, remains static, classification gradients are allowed to flow back through a,bb encourage task-relevant feature extraction during representation learning.
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Table 7: Lightweight GARLIC configurations on P12: varying window size (feature dim=16.
Midden_size=125
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
16
128
86.40
0.581
3322
16
128
85.18
0.221
953
Table 8: Lightweight GARLIC configurations on P12: varying hidden size (feature_dim=lle
time_ lag=2.
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
16
128
86.40
0.581
3322
16
64
86.18
0.279
1004
16
32
84.37
0,282
988
In the second stage, we freeze o., and update the classification module 9g based solely on classifica- tion performance:
0₂ - 0g V, Lels, with 0n,b fixed.
By alternating these steps until convergence, each module is allowed specialize its respective objective while still benefiting from shared latent features. This decoupling mitigates conflicting learning dynamics, enhances training stability, and improves overall classification performance.
Our design is inspired by DeFRCN (Qiao et al. (02T), which decouples localization and classification heads in few-shot object detection avoid multi-task interference. While their setting addresses conflict between distinct tasks, our scenario involves misalignment among sub-oba uub-objectives within single classification task due auxiliary reconstruction. Nevertheless, the underlying principle remains consistent: isolating incompatible optimization signals improves convergence and modular performance.
B
DATA DETAILS
B. DATASET DESCRIPTION
MIMIC-III We use the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, large, publicly available database comprising deidentified health-related data from over 40,000 critical care patients admitted to the Beth Israel Deaconess Medical Center between 2001 and 2012 (Johnson et al., 2016). The dataset contains high-resolution clinical information, including demographics, vital signs, laboratory test results, medications, procedures, caregiver notes, imaging reports, and in-hospital as well as post-discharge mortality outcomes. MIMIC-III supports a wide range of research applications in clinical prediction modeling, epidemiology, and decision support, and is notable for its large and diverse ICU population, high temporal granularity, and unrestricted availability to researchers worldwide.
PhysioNet Challenge 2012 (P12). The P12 dataset is derived from the PhysioNet/Computing in 2012). It comprises clinical records from 12,000 adult ICU stays spanning various unit types, including medical, surgical, trauma, and cardiac ICUs. All patients were admitted for diverse clinical conditions, and ICU stays shorter than 48 hours were excluded. Up to 42 signals are recorded per patient, including 6 admission-level descriptors and 36 time-varying physiological signals (e. g-, vital signs and laboratory results). Due to signal sampling frequencies and clinical heterogeneity, the actual number of available signals varies across patients. Each observation time-stamped with its offset from ICU admission, providing fine- fine-grained temporal resolution for modeling.
Cardiology Challenge 2012 (Silva
PhysioNet Challenge 2019 (P19). The P19 dataset is derived from the PhysioNet/Computing in Cardiology Challenge 2019 (Reyna al.) 2019),, which focuses the early prediction of sepsis in ICU settings. comprises multi multivariate clinical time series from 40,331 ICU stays, collected across three distinct hospital systems. Each patient record is formatted an hourly sampled time series with
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Table 9: Lightweight GARLIC configurations on P12: varying feature dimension (hidden size=128.
(ime_lag=2)
Feature Dim
Hidden Size
Time Lag
AUROC (%)
Time (s/iter)
Memory (MB)
16
128
86.40
0.581
3322
8
128
86.26
0.198
742
Table 10: Dataset statistics
Dataset
Samples
Sensors
Negative:Positive Labels
Missing Ratio (%)
Task
MIMIC
49380
103
43633:5747
15.00811.10
Mortality Prediction
P12
11988
36
10281:1707
94.80--1 1.51
Mortality Prediction
P19
40331
34
37400:2931
85.83-+-6.13
Sepsis Prediction
34 clinical signals, including vital signs, laboratory test results, and demographic attributes. Sepsis annotations follow the Sepsis-3 definition (Singer et al., (2066), requiring both a two-point increase in the Sequential Organ Failure Assessment (SOFA) score and evidence of infection.
B.2 DATA PREPROCESSING AND STATISTICS
This section details the preprocessing procedures for each dataset and presents the corresponding statistics in table I0 after preprocessing.
MIMIC-III. We the preprocessed version of MIMIC-III (Johnson al. 2016). following the preprocessing protocol adopted in Warpformer (Zhang et al. 2023). This version contains 103 clinical signals, comprising 61 biomarkers (e. g., vital signs and lab tests) and 42 intervention-related features (e. 8., medications and procedures), extracted from 53,423 ICU admissions. Consistent with prior work, we restrict our analysis to adult ICU stays and exclude neonatal cases. For the in-hospital mortality prediction task, we utilize all available time series data collected during the ICU stay, and discard admissions with a length of stay shorter than hours to ensure sufficient temporal context,
P12. We follow the official PhysioNet 2012 Challenge structure (Silva et al. 2012) and retain 36 physiological signals after removing static admission descriptors. For each patient, we extract all available timestamped observations from the first 48 hours of ICU stay. Categorical static features (e. g., gender. ICU type) are one-hot encoded. In in-hospital mortality outcomes are derived from the provided labels, and corrupted or blacklisted records excluded during filtering.
P19. We follow the official PhysioNet 2019 Challenge setup (Reyna et al. 2019). Each patient record parsed extract hourly measurements of 34 clinical signals, excluding static deseriptors (e. g., age, gender, unit type) and meta fields (e. g. ICU length of stay) from the time series inputs. Static features are processed separately, with categorical variables (e. g. gender) one-hot encoded into an extended static vector. Patients without any valid observations within the first 48 hours are excluded. Sepsis labels assigned according the official annotation, indicating whether any time point during the ICU stay is labeled as septic.
C EXPERIMENT DETAILS
C.I BASELINES AND HYPERPARAMETERS
We evaluate our method against wide range baseline models, covering classical RNN variants, latent variable models, and recent graph- and transformer-bae bassed time series architectures. All baseline models are trained on the same data splits and evaluated using identical metrics to ensure a fair comparison.
For each model, we prioritize using the best-reported hyperparameter settings from original publica- tions or official implementations. Only when such configurations unavailable not directly transferable to our datasets, we perform hyperparameter tuning within commonly used ranges. Specifically, the learning rate is selected from (1 [le- 5e-1, 1e-3 -3 le 1e-2), weight decay from
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{le 6, 5e-5--5 1e 5e 1e 3), and batch size from {64, 128, 256, 512, 1024) for PhysioNet (P12 and P19), and {8. 16, 32, 64, 128} for MIMIC-III. The following model-specific hyperparameter ranges applied only in the absence of recommended configurations.
RNN-Mean: RNN model where missing observations are imputed with global mean values (Che et all 2018). Model-specificc hyperparameters include the number of GRU hidden units selected from {32, 50, 64, 100} and the latent dimension from {10, 20, 32, 64).
RNN-Decay: RNN model that combines imputation of missing observations using an input decay mechanism (Che et al. 2018). Model-specific hyperparameters include the number of GRU hidden units selected from (32, 50, 64, 100} and the latent dimension from {10, 20, 32, 64).
RNN-A,: RNN model with input concatenated with the observation mask and sampling inter- vals. Model-specific hyperparameters include the number of GRU hidden units selected from {32, 50, 64, 100} and the latent dimension from {10, 20, 32, 64].
GRU-D: GRU model that applies a decaying mechanism in both input and hidden states (Che et al.. 2018). Model-specific hyperparameters include the number of GRU hidden units selected from {32, 50, 64, 100} and the latent dimension from {10, 20, 32, 64}.
ODE-RNN: RNN model that incorporates neural ODEs to model hidden states (Rubanova et al. 2019). Model-specific hyperparameters include the number of GRU hidden units from (50, 64, 100 latent dimension from {20, 32, 64), and set the number of layers in the ODE function network to (1,
L-ODE-RNN: Latent ODE model with an RNN as the encoder (Chen et al. 2018). Model-specific hyperparameters include the latent dimension from {20, 32, 64), encoder GRU hidden units from (50, 64, 100}, number of layers in the generative ODE function (gen-layers) from (1,
L-ODE-ODE: Latent ODE model with ODE-RNN as the encoder (Rubanova et al. 2019 Model- specific hyperparameters include the encoder GRU hidden units from {50, 64, 100 j. latent dimension from {20, 32, 64), number of layers in both recognition layers) and generative (gen- layers) ODE functions from 2.3
MTGNN: Graph-based model for forecasting that jointly combines graph learning, graph convolution,
and temporal convolution (Wu et al. 2020). Model-specific hyperparameters include the number of convolutional layers selected from [2,3) and the number of hidden channels from (32, 64, 128).
TTAND: Utilizes attention mechanism for continuous-ting representations (Shukla & Marlin
2021). Model-specific hyperparameters include the number of attention heads H selected from
(1,2,4 4) and the GRU encoder hidden size from {20, 32, 64, 128].
Raindrop: Graph-based model that handles irregular observations using graph neural network (Zhang al. 2022).
Warpformer: Learns time series at different scales with multiple warping and attention modules (Zhang et al. 2023). Model-specific hyperparameters include the dimensionality of hidden states D selected from (32, 64) and the number of attention layers J selected from {2, 3).
MTSFormer: Transformer-based model that utilizes time series data from four perspectives: Locality, Time, Spatio, and Irregularity (Zheng et al., 2024). Model- hyperparameters include the number of Transformer encoder layers selected from {2,3, 4} and the number of attention heads from {1, 2,24, 4}.
RETAIN: A reverse-timg time attention model originally designed for healthcare applications, which utilizes two-level attention mechanisms over input sequences (Choi et al., 2016)). Model-specific hyperparameters include the hidden layer size p selected from {64, 128, 256} and embedding size m selected from (64, 128, 256).
IMV-LSTM: model that captures temporal patterns and variable importance via a dual-stage attention mechanism within LSTM structures (Guo et al. 2019). Model-specific hyperparameters include the hidden layer size selected from {64, 128).
DA-RNN:
Dual-stage attention-based recurrent neural network that models both input features and temporal dependencies adaptively (Qin et al. 2017). Model-specifie hyperparameters include the hidden layer size selected from {32, 64, 128).
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Table 11: Ablation study.
Models
P12
P19
AUROC
AUPRC
AUROC
AUPRC
GARLIC
86,40.10.86
16,8911.75
90.96.10.84
55.29112.45
w/o Missingness Indicator
84.87:-0.85
52.79+
82.22++.89
33.56++2.42
w/o Decay Mechanism
82.20:t2.16
45.0445.81
89.50::0.34
$2.69+1.577
w/o Signal-wise Encoder
85.00 1.68
48.92:4477
89.78+00.3
52.80+1.004
w/o Signal-wise Attention
74.67+t-.88
36.70+-2.92
89.30++083
51.98+1.88
w/ Graph Message Passing
84.67:0.777
49.45+22.6
88.39:0.778
51.66:t:2.22
w/o GRU
76.90.10.93
1.8
75.26.13.73
28.4111..27
w/o Alternating Decoupled Optimization
1..3
54.95+33.79
90.08+0..58
54.36+1.44
C.2 GARLIC HYPERPARAMETERS
We organize the hyperparameters of our proposed model into two categories: model architecture and training configuration. The following parameters are tuned during experimentation only when no recommended configuration is available.
For model architecture, the window size is selected from {2. 11), which corresponds time lags (1.2.... 10} used in the local temporal context, where the effective time lag window size- The input feature dimension D is chosen from {16, 32), and the hidden state size D₁, for both encoder and decoder is selected from {128, 512).
For training, we tnne the classification learning rate over {1e-5, (1e-5,5e=) ,5-3 1e- classification weight decay from (1e-1, [1e-1,5e_1, 1e 1e-3), ), reconstruction learning rate from (1e-1, le 1e-3), and recon- struction weight decay from {lc -3, 5c -5 le- -, 5e 1e 13. The classification loss weight Ae is selected from {0.1, 5, 10). the graph regularization loss weight >g is fixed at 0.01, and the batch size is chosen from {64, 128, 256].
D ABLATION STUDIES AND SENSITIVITY ANALYSIS
D.1 COMPONENT ABLATION
To better interpret the ablation results in table 11 clarify the correspondence between each ablated variant and its associated module in GARLIC:
w/o Missingness Indicator: Removes the missingness flag my, in the augmented input This prevents the model from distinguishing observed from
vector, defined in equation 2
imputed values.
w/o Decay Mechanism: Disables the exponential decay- imputation defined equa- tion replacing time-aware estimation with static global means.
w/o Signal-wise Encoder: Replaces the signal-specinn encoders MLP in equation 3 with shared encoder, thus ignoring the signal-specific heterogeneity in feature distributions.
w/o Signal-wise Attention: Removes the cross-signal attention mechanism used at each timestep, as formulated in equation 6 thus aggregating signals uniformly.
w/o Graph Message Passing: Eliminates the inter-signal message passing step in equation disabling explicit modeling of relational dependencies among clinical variables.
w/o GRU: Removes the GRU recurrence layer in equation 7 thereby discarding local temporal continuity modeling prior temporal self-atiention.
w/o Alternating Decoupled Optimization: Trains the model jointly reconstruction and classification objectives, rather than using the alternating optimization strategy described in section 3.4
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Table 12: Dataset statistics for the Human Activity dataset.
Samples Sensors
Class Distribution
Labels Missing Ratio (%)
Task
Walking: 1271, Falling: 1160,
Lying: 896, Sitting: 2908,
6442
12
7
74.99 + 0.06
Posture Classification
Standing up: 621, all fours: 477.
Sitting on the ground: 207
Table quantifies the impact disabling each core component of GARLIC on P12 and P19. Removing the missingness indicator reduces AUROC/AUPRC by 1.5/4.1 pts on P12 and 8.7/21.7 pts on P19, showing that flagging observed vs. imputed entries is critical under high missingness. Disabling the exponentialdecay mechanism costs 4.2/1 1.8 pts on P12 and 1.5/3.2 pts on P19, un- derscoring role in capturing variablespecific dynamics. Replacing the persignal encoder with uniform encoder/attention incurs moderate losses (1.4/7.9 pts on P12. 1.2/2.5 pts P19), confirming the importance of modeling hetcrogeneity across clinical signals. Ablating signalwise attention causes dramatic 11.7/20.2 pt drop P12 (but minimal change on P19), reflecting datasetdependent sensitivity to local temporal encoding. Disabling graph message passing degrades performance by 1.7/7.4 pts on P12 and 2.6/6.6 pts P19, confirming the benefit of modeling intersignal relations. Omitting the GRU yields the largest penalty (9.5/16.0 pts on P12, 15.7/26.9 pts on P19), highlighting its necessity for temporal continuity. Finally, reverting joint endtoend training (no alternating opti- mization) leads to a modest 1. 1.1/1.9 drop on P12 and 0.9/0.9 on P19, validating that decoupling reconstruction and classification stabilizes learning.
D.2 HYPERPARAMETE SENSITIVITY FOR TIME LAG
Among the tunable hyperparameters, we focus our sensitivity analysis on the time lag т, which determines the length of the local temporal window used in both the attention and graph message passing modules. Unlike standard training hyperparameters (e.g., learning rate or batch size), T is tightly coupled with the model's architectural design and directly affects how local temporal dependencies are captured. As such, it represents a model-specific architectural choice rather than a general training hyperparameter, and therefore warrants focused analysis.
Figure 3 shows the AUROC achieved on P12 and P19 for varying values of т. We observe that performance P12 remains relatively stable across different values, while P19 exhibits clearer performance peak
0.91
P12
P19
This contrast highlights the dataset-dependdet role of 7: in longer sequences (P12), variations in affect smaller part of the temporal con- text, resulting in milder impact on performance. In shorter
AUROC
sequences (P19), the same variation significantly alters the model's 's view of input, leading to more noticeable performance differences.
Despite the general robustness of the model, the sensitivity patterns suggest that r plays non-negligible role in shap- ing temporal representations, especially in settings with lim- ited context length. These findings justify treating T as an architecture-level design choice that should be adapted to the characteristics of the dataset.
0.85
2
9
10
Lag
Figure 5: Sensitivity
E APPLICABILITY TO BROADER TASKS
Although many clinically relevant prediction problems-sude clinical deterioration prediction naturally framed as binary classification, we believe that assessing the model's performance on broader set of tasks is crucial to fully demonstrate its versatility.
To this end, we extended our evaluation to two additional directions: multi- classification and imputation. These experiments highlight GARLICs flexibility and generalization capability.
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Table 13: Performance comparison on the Human Activity dataset.
Model
Accuracy (%)
AUPRC (%)
GRU-D
87.41 + 1.21
81.29 + 2.83
ODE-RNN
86.91 1 0.66
77.11 1.88
L-ODE-ODE
86.16 0.83
74.63 3.74
L-ODE-RNN
85.71 +: 1.22
71.04 1.53
mTAND
88.72 1: 1.46
72.77 + 4.02
MTSFormer
89.24 0.53
79.14 1.46
WarpFormer
89.28 0.87
80.60 1.79
GARLIC
91.21 1.03
84.70 = 1.92
E.1 HUMAN ACTIVITY CLASSIFICATION
To evaluate GARLIC's performance multi-class classification under irregular sampling, conducted experiments on the Human Activity dataset, a widely used non-clinical benchmark for motion recognition. The dataset comprises multivariate time series collected from wearable sensors, encompassing 7 activity classes (see table [2). The task is a 7-class classification problem aimed at identifying the subjects activity from sensor readings time. Following previous works (Rubanoval et all 2019; (Shukla & Marlin (021), we applied standard preprocessing including normalization, Bliding-windoo segmentation, and label alignment.
E.2 BENCHMARKING ON PYRREGULAR
We also conducted additional experiments using the PYRREGULAR benchmark (Spinnato & Landi 2025) and compared GARLIC with ROCKET under the same experimental settings. The results are summarized in table [4
For the benchmark results, GARLIC demonstrates superior performance on the three healthcare datasets (P12, P19, MI3), confirming its strong suitability for the medical domain. For the Human Activity Recognition and sensor domains, GARLIC still achieves better performance on datasets such LPA, CT, DD, DG, and DW, highlighting its broader applicability when signals exhibit meaningful interactions.
However, in the mobility domain (AN, AOC, GS, MP, SE, TA, VE), where the input signals primarily GPS or accelerometer mcasurements, the signals do not possess strong interdependencies. As result, GARLICs capability leverage relationships among multiple signals cannot be fully utilized, leading to suboptimal performance. A similar limitation is observed on single-signal datasets (GM, GP. GX, GY, GZ, PGZ, SGZ, PL). Additionally, datasets like IW, which consist of frequency- domain data with minimal temporal correlations, are not well-suited for model designed to capture temporal interactions across signals.
These results further highlight that GARLIC is particularly effective in scenarios where multiple signals are strongly correlated and exhibit rich temporal dependencies, as is typical in ICU medical data. This aligns with the motivation of our study: the medical domain presents uniquely challenging irregular multivariate time series, where capturing interactions among signals crucial for accurate prediction. While GARLIC may be less advantageous in domains with weak independent signals, its design and performance are well-suited to healthcare applications, justifying our focus on this domain.
E.3 IMPUTATION
GARLICs modular architecture allows straightforward adaptation to tasks beyond classification. Specifically, the imputation task can be performed by disabling the third modulethe Cross- Dimensional Sequential Attention-and using only the first two components: the Latent Feature Modeling module and the Time-Lagged Graph Message Passing module. No further architectural changes necessary.
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Table 14: Comparison between ROCKET and GARLIC across datasets.
Dataset
ROCKET
GARLIC
Sign
ABF
0.17 0.00
0.32 0.02
AN
0.90 0.04
0.00
2
AOC
0.80
0.05
APT
0.96 0.00
0.76
3
ARC
0.99
0.00
CT
0.98 0.00
0.000
DD
0.54
0.00
DG
0.34
0.75
DW
0.42
0.00
GMI
0.66 1.66+0.00
0.05
GM2
0.57
0.28
GM3
0.48
0.20
GPI
0.02
0.71
GP2
0.85
0.65 0.05
GS
0.31
2
GX
0.70
0.49 0.02
GY
0.70
+
GZ.
0.10
0.00
IW
0.00
0.02
200
JV
0.94
0.00
12
LPA
0.00
0.49
12
MI3
0.07
17
MP
0.00
0.92
P12
0.00
0.68 0.00
37
P19
0.71
0.77
34
PAM
0.10
52
PGE
0.00
0.78 1.00
9
PGZ
00.00
0.70
I
PL
0.85
0.24
SAD
0.000
0.02
13
SE
0.80
0.12
4
SGZ
0.88 0.02
0.06
TA
0.01
0.07
2
VE
0.94 0.02
0.94 0.03
To assess this capability, carried out imputation experiment on the P12 dataset, in which fraction the observations was randomly masked and used as ground truth. The model was trained to reconstruct missing values using mean squared error (MSE) the loss function. We compared against several representative baselines supporting time series imputation, including L-ODE-RNN, L-ODE-ODE, and mTAND. The results presented in table 15
LEARNED TIME-LAGGED SUMMARY GRAPH
To better understand how our model captures inter-signal dependencies for local reconstruction, we analyze the learned time-lagged summary graph G, encoded by the adjacency matrix W.. While the primary goal of our model outcome prediction, the graph module plays a crucial auxiliary role by modeling short-range temporal and cross- siisal structure. Investigating the properties of the learned graph can thus shed light on the nature the dependencies captures.
We observe that the learned adjacency matrix W, varies across different random seeds. This variabil- ity arises from the redundancy inherent in physiological signal dependencies and non-identifrable graph learning coupled with message passing (Yu al.] Compton al.) (022). Since model employs & regularization (Lasso) to promote sparsity, it tends to select minimal subsets of edges sufficient for local reconstruction. Given the multitude of potential interactions among variables,
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Table 15: Imputation performance on the P12 dataset under varying missing rates. Models were trained using MSE loss.
Missing Rate
Model
MSE
MAE
L-ODE-RNN
29.0073 + 0.4153
21.3054 + 0.1788
L-ODE-ODE
27.0088 0818
20.8137 0.1605
0.50
mTAND
12.3396 + 0.1397
10.2598
0.0677
GARLIC
12.6943 0.4066
10.6809 0.0992
L-ODE-RNN
23.2620 + 0.5398
17.0901 + 0.1745
L-ODE-ODE
21.5457 0.3561
16.6419 0.1461
0.40
mTAND
9.3711 + 0.1347
8.0737 + 0.0720
GARLIC
9.5636 = 0.1841
8.1145 - 0.0940
L-ODE-RNN
17.3302 0.5478
12.8183 0.1454
L-ODE-ODE
16.0021 0.1276
12.4869 0.0917
0.30
mTAND
6.9898 + 0.1088
5.9852 + 0.0531
GARLIC
6.6576 0.1494
5.8061 0.0797
L-ODE-RNN
11.6055 0.2748
8,5410 0.1130
L-ODE-ODE
10.7941 = 0.1971
8.3196 = 0.0511
0.20
mTAND
4.5238 + 0.0718
3.9589 0.0276
GARLIC
4.3584 + 0.2372
3.7085 0.0525
L-ODE-RNN
5.7913 0.1942
4.3054 0.0448
L-ODE-ODE
5.3364 + 0.0626
4.1734 + 0.0353
0.10
mTAND
2.2459 0.1128
1.9778 0.0161
GARLIC
1.9737 + 0.1376
1.7834 = 0.0312
multiple sparse solutions can achieve comparable reconstruction performance. Consequently, each training run may converge different sparse instantiation from a set approximately equivalent solutions. Importantly, note that the learned graph G, should be interpreted not recovery of the full physiological dependency network, but rather as a task-specific subgraph sufficient for local reconstruction. The graph is supervised it is not directly optimized using ground-truth labels, but instead emerges as an intermediate construct through the auxiliary reconstruction objective. As such, a functional tool enhance representation learning, rather than target of inference itself. The variability in learned edges across seeds thus reflects the presence of multiple valid subgraphs that support the
functional goal, rather than instability in model behavior.
To extract a more stable representation of inter-signal relationships, we compute the element- average of the learned adjacency matrices across five random seeds. Visualizations of this averaged graph are provided in figure 7 where the edge thickness reflects the mean weight learned. The resulting structure highlights consistent dependencies among physiological signals, capturing both intrinsic correlations and patterns indicative of external interventions. These observations suggest that our model effectively represents both endogenous signal dynamics and exogenous clinical actions that influence patient state.
To further assess the plausibility of the time-lagged summary graph, we conducted a post-hoc evaluation using five large language models (LLMs) proxies for expert judgment, leveraging their encoded medical knowledge. Each edge was evaluated via a standardized prompt to ensure consistency (see Box F). While not substitute for clinical validation, this provides structured plausibility check.
Results indicate that four out of five LLMs judged over 80% of edges reasonable. Notably, 9 edges were endorsed by all models and edges by four models, demonstrating strong consistency. These findings align with established medical knowledge and support the plausibility of the learned graph.
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Table 16: LLM-based post-hoc plausibility assessment of edges in the time-lagged summary graph Each edge is categorized as Reasonable, Unclear, or Unreasonable.
Model
Reasonable
Unclear
Unreasonable
Claude Opus 4
11
Gemini 2.5 Pro
Grok 3
13
0
ChatGPT 03 (Web Search)
13
DeepSeek (DeepThink R1, Search)
13
PaCO2
SBP
Temp
HC03
BUN
HR
Resp
Fi02
MAP
Lactate
Figure 6: Visualization of learned graph after edge gating
Figure 7: Learned Summary Graph Visualization. Summary graph learned on dataset P19, averaged over 5 random seeds with a time lag of 8 hours.
Prompt for LLM-based Plausibility Assessment
You are a clinical expert with deep knowledge in human physiology, critical care, and multimodal patient monitoring. We are analyzing time-lagged summary graph derived from ICU monitoring data over 9-hour window. Each directed edge represents potential influence whether physiological, systemic, or intervention-basee These influences may be direct or indirect, including multi-step pathways, systemic feedback, compensatory mechanisms, or clinical decisions. A variable does not need to the sole or primary cause for its effect be valid edges may reflect partial, synergistic, or contributing influences within complex ICU dynamics.
between two variables.
Your task is to assess the plausibility of each edge causal contributory effect, explicitly considering: Direct physiological mechanisms Indirect or multi-factorial pathways Feedback loops and compensatory responses Cascading effects of interventions and systemic derangements
For each edge, respond with: Reasonable if plausible via any direct, indirect, partial, or synergistic mechanism Unclear if the pathway is highly uncertain or not supported by known mechanisms Unreasonable if contradicts established physiological or clinical understanding
Provide a brief explanation (12 sentences), highlighting plausible direct or indirect routes, systemic interactions, clinical reasoning.
Edges to evaluate: Resp -> HR, Resp - FiOz, HR - FiO,, HR - MAP, FiO, - MAP, MAP >> Lactate, SBP + HR. SBP > FiO,, SBP - Temp. SBP - BUN, SBP - PaCO,, HCO, + Temp, HCO, + BUN, Temp - BUN, Temp + FiO,, BUN + FiO,
Future Work for Graph Learning: To address non-identifiability inherent sparse graph discovery, lightweight physiological priors such known organ system linkages treatment intervention markers can be incorporated to bias graph learning towards clinically plausible edges, and to explore uassesus-bbsed aggregation for more stable subgraphs. Extending G, dynamic, window-free
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setting can allow the adjacency weights to evolve continuously time rather than be tied to fixed lags. Finally, static patient variables and multiscale time lags can also be integrated into a unified graph framework, so that both enduring patient traits and fast-changing physiological interactions can be captured single, interpretable structure.
G STATISTICAL TEST DETAILS
We employ two complementary statistical procedures to evaluate the interpretability of attribution scores under perturbation-baser masking.
G.1 PAGES L TEST FOR ORDERED ALTERNATIVES
Suppose we have repeated runs (blocks) and k ordered conditions (here k = 4 for scenarios (c, d, b, a)). To test the strict ordering c <
a, proceed as follows:
1. For each run j .,.... n, rank the performance scores k that the smallest score receives rank and the largest rank k. Denote the rank of condition i in run j by Tij.
2. Compute the rank sums for each condition:
R
Túj: i= i k.
3. Assign weights w, corresponding the hypothesized order (c,d,b, + (1,2,3,4).
4. Compute the Page statistic
Σ wi R..
5. Under the null hypothesis of no ordering, L can be compared to tabulated critical val- ues (Page 1963), approximated by a normal distribution with
n (k: + 1)a
n (k 1)(2k + 1) (k 1)(2k + 1).
[[L]
Var(L)
24
5
yielding
L L- BiL
RJ 1),
Var(L)
6. Reject the null (and conclude a significant increasing trend c < d < a) if L exceeds the critical value or if Z > 21-a-
G.2 Two ONESIDED TESTS (TOST) FOR EQUIVALENCE
To test whether two paired conditions (e. g. full vs. top50%) are practically equivalent within margin
8 > we perform onesided f-tests:
1. Compute the paired differences d, - - for = 1,..,, their mean d and standard
error SE = =sa/vi nn.
2. Test Hot: A - versus HAI: A> -8 -5by
d
ti = SE
with n 1 degrees of freedom,
obtaining onesided = - -Fnn.1 (t1).
3. Test Hog: A 2 +5 versus HAZ: A < +5 by
8
t2 = SE
P2
Fi...
4. The TOST pvalue max(pi.p) We declare equivalence (i.e. reject the union null) if both Pi and p2 <
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H ADDITIONAL CASE STUDIES
H.1 SUPPLEMENTAL CASE STUDIES
To qualitatively assess the interpretability of our model, we select one correctly classified positive sample and one correctly classified negative sample from each of the P12 and P19 datasets. This yields four representative cases that allow us to examine the models attribution behavior under both high-risk and low-risk scenarios,
For each sample, we visualize the following components:
This visualization displays the raw attribution scores assigned to each observed value. Observation values are normalized within each signal to enable consistent visual comparison across features. The size of each marker reflects its corresponding importance score.
Importance Map of Observations (Raw):
Predicted Mortality Probability over Time: We plot the model's predicted risk trajectory
by feeding truncated input sequences of increasing length during inference, thereby revealing how prediction confidence evolves over time.
Importance per Signal:
We compute the aggregated importance scores for each signal by summing over all its observed time points, providing global view of signal-level attribution.
To reveal fine-grained temporal patterns, we normalize attribution scores within each signal channel. At this stage, we intentionally skip the redistribution step described in equation II and directly apply the attribution mask. This is because redistribution is only used to evenly reassign the importance originally attributed to imputed values back to the observed data within the same signal. Since our goal here is to examine the temporal structure of observed inputs, omitting redistribution helps avoid diluting the attribution signal and provides a clearer view of temporal dynamics. the resulting visualization, color intensíty represents importance: red indicates high attribution, while blue denotes relatively low importance.
Normalized Per-Signal Importance Map:
All visualizations include only signals with observed values; signals with no observations are omitted for clarity.
P12 Dataset Signals. Albumin (g/dL), ALP (IU/L), ALT (IU/L), AST (IU/L), Bilirubin (mg/dL), BUN (mg/dL), Cholesterol (mg/dL), Creatinine (mg/dL), DiasABP (mmHg), FiO, (0-1), GCS (score), Glucose (mg/dL). HCO, (mmol/L), HCT (%), HR (bpm), K (mEq/L.) Lactate (mmol/L., Mg (mmol/L., MAP (mmHg), Mech (0/1), Na (mEq/L.), NIDiasABP (mmHg), NIMAP (mmHgg, NISysABP (mmHg), PaCO, (mmHH), PaOg (mmHg), pH (0-14), Platelets (cells/nL), RespRate (bpm), SaO2 (%), SysABP (mmHg), Temp ("C), TropI (ug/L), TropT (ug/L), Urine (mL). WBC (cells/nL).
P19 Dataset Signals. HR (bpm), O2Sat (%), Temp (C),, SBP (mmHg), MAP (mmHg), DBP (mmHg), Resp (breaths/min), EtCO2 (mmHg), BaseExcess (mmol/L), HCO, (mmol/L), FiO, (%), pH, PaCO, (mmHg). SaO2(0 (%), AST (IU/L), BUN (mg/dL), Alkalinephos (IU/L), Calcium (mg/dL), Chloride (mmol/L.), Creatinine (mg/dl.), Bilirubin_dires (mg/dL.), Glucose (mg/dL), Lactate (mg/dL., Magnesium (mmol/dL), Phosphate (mg/dL), Potassium (mmol/L), Bilirubin total (mg/dL), TroponinI (ng/mL), Het (%), Hgb (g/dL), PTT (s), WBC (10)/ Fibrinogen (mg/dL), Platelets (10%/,L).
H.2 DISCUSSION
Our case study reveals nuanced insights into how the model assigns and utilizes feature importance across different granularity levels, offering faithful interpretation of its decision-making process in clinical time-series data.
Global-Level (Raw) Importance Attribution. We observe that signals with fewer observations tend to receive disproportionately higher per-observation importance scores. This reflects natural behavior: when a channel is sparsely sampled, each observation contributes a larger fraction of the total available information for that signal, prompting the model rely more heavily these sparse events. In positive samples (Sample02 and Sample04), these sparse observations frequently coincide with sharp increases in prediction probability, indicating their critical role in outcome prediction.
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Importance Spores
8004
Normal ued
cop
Augresgltes
Probobikyy
Monsily Probasiity
Time (minutes)
(a) Importance Map of Observations (Raw)/ Predicted Mortality Probability
(b) Importance per Signal
Figure 8: Sample 01: Surviving patient from P12.
8
Deeretter h000
Figure 9: Sample 01: Normalized per-signal importance map.
Conversely, in negative samples (Sample01 and Sample03), similar observations occur during stable, low-risk phases and do not cause prediction hinkks--ighlighting their role in characterizing patient stability. These patterns suggest the model is not merely reactive to anomalies, but rather context- aware in distinguishing between deterioration and normaley.
Signal-Level Importance Distribution. When aggregating importance scores across each signal, we find relatively small variance (within 5%%10%), indicating that the model does not rely excessively on a few dominant signals. Instead, it integrates information across multiple signals in a synergistic manner. This likely reflects two key modeling traits: (1) preference for global pattern recognition
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Normal lood Volue
10
Probabiity 5.5
Precicted Mortality Probubiing boy
(minutes)
Signal
(a) Importance Map of Observations (Raw)/ Predicted Mortality Probability
(b) Importance per Signal
Figure 10: Sample 02: Non-surviving patient from P12.
Wrcotanted Connution chicn
Mocttase Sesmaton 1030
Cleeridees
mnsemets 2 Coeencders 20e6
Inpersooo Cleer iders AEC.
Figure 11: Sample 02: Normalized per-signal importance map.
rather than localized feature triggers; and (2) limited granularity in distinguishing signal-specific importance, possibly due to generalization bias to avoid overfitting to any single input source. Such behavior is desirable in complex clinical settings, where predictive stability is favored over fragile saliency.
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Normalized Value
Scatoo
8050
Predicted Sepii Probabilty
Probability
0.6
2000
6240
02
630
16 38 20
Time (hours)
Signal
(a) Importance Map of Observations (Raw)/ / Predicted Sepsis Probability
(b) Importance per Signal
Figure 12: Sample 03: Non-sepsis patient from P19.
anpormnnc 20
Figure 13: Sample 03: Normalized per-signal importance map.
Temporal-Level Sensitivity (Normalized Importance). After normalizing importance at the signal level, we find the model exhibits strong temporal sensitivity to two specific types of patterns. First, it events-abrus changes often corresponding acute physiological
reacts strongly to high-variatinn deterioration. For instance, in Sample02, the model highlights spikes in HR, MAP, and DiasABP, as well as increasing fluctuations in NISysABP, NIDiasABP and NIMAP. Second, the model emphasizes abnormal value onsets, such as HR 90, temperature 38 and WBC < 4 in Sample04-mare markers that are clinically associated with onset of sepsis. These behaviors indicate the model is sensitive to early signs of clinical instability, validating its prioritization from a physiological standpoint.
Temporal Alignment with Clinical Semantics. Interestingly, we find that the temporal distribution of feature importance naturally aligns with the models prediction direction, revealing contrasting attribution patterns for different outcomes. For example, in the GCS channel, the model assigns greater importance to rising trends and values above 8 in Sample01 (predicted survivor), whereas
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Normal Value
08
Predicted Supis Probability
Prebabilty
96
00
Time (houns)
(a) Importance Map of Observations (Raw) / Predicted Sepsis Probability
Importance per Signal
Figure 14: Sample 04: Sepsis patient from P19.
Corre
Figure 15: Sample 04: Normalized per-signal importance map.
highlights declining trends and values below in Sample02 (predicted as a non-survivor). This indicates that the model does not simply react to specific values or patterns in isolation, but rather emphasizes temporal segments that are most indicative of the predicted class. Similar patterns are observed in Sample01 for FiO, and temperature, where segments following a transition into normal ranges receive higher importance compared to those in abnormal ranges- model focuses on evidence of physiological recovery when predicting survival. These attribution behaviors also align well with the dynamics of predicted probabilities, as periods of high attribution typically coincide with sharp probability shifts, particularly in positive cases. Collectively, these observations demonstrate that the model assigns importance in a way that is both temporally grounded and semantically consistent with its outcome predictions.
that the
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Limitations and Potential Improvements of Interpretability. While many of GARLIC's attributions align with clinical intuition, there are instances where high importance is assigned to physiologically stable measurements, raising questions about interpretability. Specifically, this may occur due to noise or instability in the attribution process, or because GARLIC captures statistically correlated patterns that lack clear clinical meaning, often serving as indirect proxies for latent-space reconstruction temporal encoding rather than directly predicting outcomes.
However, this behavior is mainly observed in true-negative (healthy) samples, such as Sample 03 in figure 13, where highlighting stable values is physiologically reasonable. For example, GARLIC emphasizes measurements around hour 21 HR. SBP, MAP, O2Sat, and Resp. Although is unclear why these particular timepoints are highlighted over similar stable points, stable physiological values non-sepsis cases provide evidence against clinical deterioration, suggesting that such attributions are both reasonable and clinically meaningful.
To improve the clinical consistency GARLICs interpretations, we propose three complementary strategies: Clinical Priors Integration: Incorporate expert-defined physiological ranges (e.g.. normal MAP or WBC intervals) via an currbution-regularizatoon loss, which penalizes high importance assigned to normal values and emphasizes clinically significant anomalies. Applicabiliy: When robust clinical priors are available. Limitation: Effectiveness depends on prior quality and may miss rare pathological events complex interactions outside standard ranges. Stability-Aware Attribution Filtering: Apply temporal smoothness constraints to attention weights to reduce isolated spikes and emphasize coherent trends. Applicability: When signals densely sampled and temporal continuity is important. Limitation: May obscure brief but clinically relevant deviations; not suitable for sparse data. Gradient-Based Attention Exploration: Augment attention with gradient-based saliency methods (e.g., Grad-CAM-style) to capture fine-grained dependencies (Leem & Seo 2024 Barkan et al.) 2021). Applicability: When detailed feature interactions are critical and sufficient computational resources are available. Limitation: Sensitive to noise in dish--dmensional data and introduces additional computational overhead.
In summary, GARLIC provides foundational attribution mechanism for clinical time series, capable of capturing both risk-elevating and stabilizing patterns across signals and time. Our case studies show that the model attends clinically meaningful events, differentiates attribution behaviors across predicted outcomes, and distributes importance in a way that largely aligns with clinical intuition. To further enhance interpretability and reliability, we propose three complementary extensionselinical priors integration, stability- aware filtering, and gradient-base based saliencywhice offer flexible paths to adapt the attributions based signal characteristics and computational constraints. While some attribution inconsistencies remain, these analyses demonstrate the potential of structured temporal attributions to provide actionable insights and reinforce trust in clinical decision-making systems.
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Reviews:
[{"review_id": "wM5TJuFy2e", "reviewer": "Reviewer_5UZn", "summary": "The paper introduces GARLIC, a model designed for ICU time-series data characterized by irregular sampling and substantial missing values. It consists of four main components:\n\n•A decay-based imputation module for handling missing data.\n\n•A time-lagged graph network that enables information exchange among signals.\n\n•Signal and temporal attention layers, separated by a GRU, to highlight the most relevant signals and time points.\n\n•An alternating training strategy that switches between reconstruction and prediction objectives.\n\nThe model is evaluated on the P12, P19, and MIMIC-III datasets, where it demonstrates superior AUROC and AUPRC performance compared to baseline methods. Additionally, the authors include an interpretability test, similar to ROAR, to assess whether the attention mechanisms emphasize clinically important features.", "questions": "•Did you make sure to avoid patient or hospital overlap between training and test sets?\n\n•For MIMIC-III, since you use “all ICU data,” how do you ensure the model doesn’t use information collected after the outcome?\n\n•How sensitive are your results to the uniform redistribution rule used in your interpretability part (Eq. 11)?\n\n•Did you try a simpler baseline where you just combine decay imputation + GRU without the graph to see how much the graph actually helps?\n\n•How does the model perform when using only the first 24 or 48 hours of data? This would test if it’s useful for early prediction.\n\n•Are the edges in your learned graph constrained or reviewed for clinical meaning?\n\n•Did you test robustness for different missingness rates or lag values (τ)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "•Addresses a challenging and important issue in clinical data: irregular and missing time-series signals.\n\n•The model design is clear and well-motivated, effectively combining graph reasoning with atteq1Qntion mechanisms.\n\n•Evaluation is conducted across multiple datasets, demonstrating consistent performance.\n\n•The inclusion of an interpretability test adds value by assessing whether the attention weights align with clinically meaningful features.", "weaknesses": "•The novelty feels limited; it’s mostly a combination of existing parts (decay imputation + graph + attention + alternating training). It’s more of an engineering improvement than a new concept.\n\n•The data split setup might cause data leakage. For example, in MIMIC-III, they say they use “all available ICU data,” which can include info after the event, making prediction easier but unrealistic.\n\n•There’s no patient-level or hospital-level split, which means the model might just memorize patterns from the same patients or hospitals instead of generalizing.\n\n•The interpretability approach is weakly justified. You say “we redistribute the imputed contributions uniformly” across observed values — but this is just a heuristic and might distort the results.\n\n•The benefit of alternating training seems small compared to its added complexity.\n\n•They claim “energy-efficient communication and comparable runtime,” but there’s no detailed info on hardware, fairness, or batch sizes, so it’s hard to trust the runtime comparison.\n\n•Calibration and clinical usefulness (like early prediction or alert timing) are not discussed, which is important for ICU tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R9ra9fvxXB", "reviewer": "Reviewer_QH9P", "summary": "This paper proposes a multivariate time series learning method for intensive care. The proposed method first impute the missing value in the time series by an exponential-decay mechanism, and then apply the attention mechanism across the feature and time dimensions to learn the final representations. A data attribution method is also proposed to estimate the contribution of features at different timestamps by propagating the attention weight back to the input multivariate time series.", "questions": "Please see the Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The proposed method not only achieves strong empirical performance but also demonstrates integrated interpretability, effectively addressing the needs of the intensive care problem under study.\n- Comprehensive experiments, including baseline comparisons, ablation studies, sensitivity analysis, and case studies, are conducted to validate the effectiveness of the proposed method and substantiate its claims.", "weaknesses": "- The contribution of this paper is somewhat incremental. The exponential-decay mechanism , interleaved attention across time and feature dimensions, and estimate the importance of data by attention weights are common techniques in multivariate time series learning. The proposed method seems to be a combination of these existing techniques, lacking unique adaptations tailored to the specific problem being addressed.\n- For Table 9, further clarification is needed regarding the dramatic performance drop when the GRU module is removed.\n- The notations in the method section can be improved: 1) All the attention module is denoted by a “xxAtten” text, the author should more clear introduce the computation procedure. For example, what is the q,k,v of the SignalAttn, and how  $u_{k,t}$ is computed in Equation (6). 2) Equation (9) seems problematic, where the subscript t only appear in the right side.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fmPUCUrX3c", "reviewer": "Reviewer_y3mL", "summary": "This paper investigates multivariate time series data from Intensive Care Units (ICUs) and addresses key challenges identified in prior research, including the inherent irregularity and heterogeneity of clinical measurements, as well as the limited interpretability of existing approaches. To tackle these challenges, the authors propose the Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, which incorporates dedicated mechanisms to enhance analytic performance and provide integrated interpretability. The proposed model is empirically validated on three real-world ICU datasets, demonstrating its efficacy in improving predictive performance and providing meaningful interpretations.", "questions": "Beyond W1-W3, I have the following questions:\n\n**Q1.** In the performance–efficiency trade-off analysis presented in Appendix A.2.3, it is observed that the P19 dataset is used in Table 4 for the evaluation under varying time lag settings, whereas the P12 dataset is adopted for the other three varying configurations. It would be helpful to clarify the rationale behind these dataset-specific choices.\n\n**Q2.** The main ablation study in Appendix D.1 is conducted on smaller datasets rather than the largest one, MIMIC-III. What is the underlying rationale for this choice? Performing ablation on the most representative or largest dataset would provide stronger evidence for the robustness and generalizability of the proposed model.\n\n**Q3.** Regarding the imputation results, it is shown that the baseline mTAND outperforms GARLIC under high missing rates and performs comparably under low missing rates. This observation may suggest potential limitations of GARLIC in handling severe missingness. Further analysis or discussion explaining this behavior would be helpful for clarification.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**S1.** The paper identifies representative challenges associated with multivariate time series data from ICUs. The proposed GARLIC model is well designed to address these issues through (i) a decay-based imputation mechanism to handle irregular missingness, (ii) a self-attentive, time-lagged graph learning mechanism to capture spatial dependencies, and (iii) a cross-dimensional attention mechanism to integrate temporal signal embeddings. These design choices appear technically sound and well aligned with the stated objectives.\n\n**S2.** The experimental evaluation is comprehensive, assessing the efficacy of GARLIC from multiple perspectives, including comparative performance against strong baselines, computational efficiency, ablation studies, sensitivity analyses, and evaluation on broader downstream tasks.\n\n**S3.** The interpretability aspect of GARLIC is examined both quantitatively, through a perturbation-based evaluation strategy, and qualitatively, via representative case studies. These analyses effectively demonstrate the model’s potential to provide clinically meaningful insights alongside strong predictive performance.", "weaknesses": "**W1.** The identified challenges—irregular sampling, heterogeneous measurements, and limited interpretability—are not unique to ICU data but are generally characteristic of electronic health record (EHR) data. It would therefore be beneficial for the authors to clarify the rationale for restricting the study’s scope to ICU data. Specifically, it would strengthen the paper to discuss why the broader context of EHR analytics was not adopted as the primary focus, and how the proposed methods and findings relate to, or differ from, existing studies on general EHR data.\n\n**W2.** The key architectural components integrated into GARLIC, including the Time-Lagged Graph Message Passing and Cross-Dimensional Sequential Attention mechanisms, appear to be largely drawn from prior research in this domain. As a result, the methodological novelty of the model seems limited. The paper would benefit from a more explicit articulation of how GARLIC advances beyond existing approaches and what its tangible contributions are beyond the combination of established techniques.\n\n**W3.** Given that this work concerns ICU data analytics, a high-stakes and clinically sensitive application domain, it would be valuable for the authors to elaborate on the current stage or planned trajectory toward real-world deployment. A clearer description of practical considerations, such as model validation in live settings, potential integration with clinical workflows, and collaboration with medical practitioners, would significantly enhance the work’s translational relevance and credibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aWGI4nqEzI", "reviewer": "Reviewer_1Z9E", "summary": "This paper introduces a graph attention-based relational learning framework, termed GARLIC, designed for the classification of irregular multivariate time series in intensive care. The GARLIC model incorporates decay-based imputation, time-lagged graph learning, and cross-dimensional attention mechanisms to effectively address irregular missingness and capture complex inter-variable dependencies. Experimental evaluations on the P12, P19, and MIMIC-III medical datasets demonstrate that GARLIC outperforms existing baseline methods in classification performance.", "questions": "1.\tAccording to the experimental analysis in Reference [1], the Rocket algorithm for regular time series classification significantly outperforms several irregular time series classification baselines. Could the authors provide a comparative analysis of Rocket versus GARLIC under similar experimental settings as in Reference [1]?\n\n2.\tReference [1] adopts the macro-averaged F1 score as an evaluation metric for imbalanced classification tasks. How do the strongest baseline methods in this paper, as well as Rocket, perform compared with GARLIC in terms of the F1 metric?\n\n3.\tBased on the dataset organization for irregular time series classification in Reference [1], could the authors clarify why the study focuses exclusively on the medical domain? Alternatively, how does GARLIC perform compared to Rocket across all benchmark datasets summarized in Reference [1]?\n\n[1] PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks. arXiv, 2025.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tThe paper is clearly written, with a well-structured and persuasive review of related work. The experimental analysis and discussion are comprehensive and insightful.\n\n2.\tThe study focuses on the challenging task of irregular multivariate time series classification, effectively addressing both the modeling of irregular temporal patterns and the dependencies among variables. This contributes valuable insights to the development of methodologies in irregular time series classification.\n\n3.\tThe integration of attention mechanisms to enhance interpretability in irregular time series classification represents a notable strength of the work.", "weaknesses": "1.\tThe study focuses solely on the intensive care medical domain, despite irregular multivariate time series also being prevalent in areas such as human activity recognition, mobility, and sensor data [1]. The model’s motivation and design appear not strongly tied to the medical scenario.\n\n2.\tThe proposed GARLIC model largely follows the GRU-D approach for irregular pattern learning and employs common graph- or attention-based structures for variable dependencies modeling, limiting its novelty.\n\n3.\tWhile interpretability is emphasized, the analysis only identifies important features without clearly linking them to irregular patterns or inter-variable relationships. The rationale for selecting a 50% masking ratio is also insufficiently justified.\n\n\n4.\tThe case study in Figure 3 is difficult to understand, relying heavily on medical terminology without citations, which may hinder comprehension for readers unfamiliar with the P12 and P19 datasets.\n\n5.\tThe strategy of imputing irregular time series into regular ones is straightforward, but the baseline methods used for comparison do not include models designed for regular time series classification.\n\n\n\n6.\tThe absence of released source code limits the reproducibility of experimental results.\n\n[1] PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks. arXiv, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)