NeurIPS 2025 Past Healthcare & biologyTime series
NeurIPS 2025 Workshop on Learning from Time Series for Health
TS4H NeurIPS 2025
- Submission deadline
- Sep 3, 2025, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (96)
Fetched from OpenReview (v2) on 2026-06-10.
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4 Hz, 4 Pages: Just-in-Time Substance Use Relapse Risk Detection from Wearable Time Series Data
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A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis
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A novel approach to classification of ECG arrhythmia types with latent ODEs
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A Second-Order SpikingSSM for Wearables
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A study on intensive care early event prediction: How well do clinicians perform against AI?
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A Time Series Foundation Model for Cancer Management
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A Time-Series Vision–Language Model for Predicting Progression of Diabetic Retinopathy
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ACUMEN: Active Cross-Entropy Method with Uncertainty-driven Neural ODEs for Data-Efficient System Identification in Healthcare
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AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data
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Autoregressive ConvLSTM Framework for fMRI Time Series Forecasting in Alzheimer’s Disease
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Biological Pathway Informed Models with Graph Attention Networks (GAT)
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Black Box to Bedside: Distilling Reinforcement Learning for Sepsis Time Series
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Blind Source Separation for Fetal PPG with Rate-Based Proxy Supervision
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CAND: Cross-Sign Ambiguity Inference for Early Detecting Nuanced Illness Deterioration
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CAST: Causal Modeling of Time-Varying Treatment Effects on Head and Neck Cancer
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Causal Emergent Representation Learning Under Distribution Shift in Critical Care Time Series
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Causal Representation Learning from Multimodal EHRs under Non-Random Modality Missingness
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Collective Data Bargaining for Fairness in Health Time Series AI
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Computationally Efficient and Generalizable Machine Learning Algorithms for Seizure Detection from EEG Signals
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Contrastive Learning for Multi-Label ECG Classification with Jaccard Score–Based Sigmoid Loss
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Contrastive Time Series Representation Learning for Neurochemical Concentration Prediction
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Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling
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Decoding Type 2 Diabetes Progression via Metabolic Hormone Time-Series
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Don’t Sleep on Sleep Data: Influence of Sleep Physiological Signals on Stress Detection
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Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis
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DuLPA: Dual-Level Prototype Alignment for Unsupervised Domain Adaptation in Activity Recognition from Wearables
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dynAmiC: Dynamic Domain Adaptation with Efficient Coreset Selection
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Early Warning of In-Hospital Cardiac Arrest from Photoplethysmography Using Deep Residual Networks
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ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
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Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model – a UNIPHY+ Approach
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Evaluating Language Models as Descriptors of Neonatal Heart Rate in Mortality Prediction
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Explaining Temporal Effects in Sepsis Prediction
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Exploring multi-site dataset shifts in electronic health records using time series features
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Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment
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Flow-Guided Neural Operator for Self-Supervised Learning on Time Series Data
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Foundation Models for Hemodynamic Time Series: A New Paradigm in Cardiovascular Data Modeling
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GI-Clust: Deep Clustering for Early Gastrointestinal Cancer Detection
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Healthcare TimeSeries Reasoning Benchmarks at Scale
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High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
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Improving Forecasts of Suicide Attempts for Patients with Little Data
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Integrating Slow Neural Oscillations and Physiological Burden for Trait Anxiety Prediction
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Interpretable Graph Learning on Irregular Clinical Time Series
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JETS: A Self-Supervised Joint Embedding Time Series Foundation Model for Behavioral Data in Healthcare
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Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data
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Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
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Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads
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Mapping the Dynamics of Atrial Fibrillation with Spatiotemporal Graph Neural Networks
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Multi–Cancer Risk Prediction Using Transformers Trained on Large-Scale Longitudinal EHR Data
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Neo-InstructTime: Editing Deceleration Events in Neonatal Vital Signs Using Natural Language
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NEUROSKY–EPI: The First Open Single–Electrode Epilepsy EEG Dataset with Context–Aware Modeling and Clinically Grounded Metadata
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No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series
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Non-invasive electromyographic speech neuroprosthesis: a geometric perspective
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PathoFM: Toward a Foundation Model for Pathological Gait
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Peak-R1: Instruction-Tuned Large Language Models for Robust J-Peak Detection in Cardiomechanical Signals
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Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification
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PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation
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Predicting Dementia Risk Using Longitudinal Electronic Health Records Data
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Predicting Mortality in ICU Patient with Hypertension Using Machine Learning on EHR Data
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Pretraining Patient Foundation Models on Multimodal Patient Journeys
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Probabilistic Digital Twin for Data-driven Smart Weaning of Mechanical Circulatory Devices
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Projection-based Robust Interpretable Signal Mixing for Remote Heart Rate Estimation
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Prune or Quantize? Layer-wise Compression of Time-Series ECG Foundation Networks
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PULSE-LAB: A Multimodal Hybrid State-Space Model for Forecasting the Presence of Thoracic Pathologies from ECG Time Series and Laboratory Data
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RAF: A Model Agnostic Framework for Retrieval Augmented Zero Shot Time Series Forecasting
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RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification
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Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control
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Riemannian Transfer Learning in Motor Imagery decoding: Reproducibility and Standardized Benchmarks
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RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
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Safe Active Learning of Cerebrospinal Fluid Dynamics
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Segment-Then-Connect: Change Point Dynamic Connectivity for Early MCI Detection
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Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for Foundation Models
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Self-Supervised Learning for Gestational Age Estimation from Low-Cost Doppler Ultrasound in Low-Resource Settings
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Signals of Decline: Machine Learning driven Biomarkers for Alzheimer’s Disease
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SleepLong: Towards Generating Long-Sequence Sleep Heart Rate Signals with Conditional Diffusion
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Speech Foundation Models Generalize to Time Series Tasks from Wearable Sensor Data
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SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management
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Subject-Aware Contrastive Learning for EEG Foundation Models
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Task-Aware Functional Hypergraph Learning for Brain State Classification via Information Bottleneck
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Temporal Gaze Dynamics as Zero-Shot Prompts for Volumetric Medical Segmentation
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TIDAL: A Temporal Causal Diffusion Framework for Visualizing Knee Osteoarthritis Treatment Outcomes
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Time-Aware Synthetic Control
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Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
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Towards Characterizing Knowledge Distillation of PPG Heart Rate Estimation Models
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Towards Evaluating Robustness of EEG-based Sleep Trackers
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Towards On-device Foundation Models for Raw Wearable Signals
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Towards Self-Supervised Foundation Models for Critical Care Time Series
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Transferring Clinical Knowledge into ECGs Representation
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Transient Neural Dynamics Reconstruction
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Translating Deep Learning to Clinical Practice: External Validation and Clinical Benefit of an Electrocardiogram-Based Neural Network for Detecting Low Ejection Fraction
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TrendGNN: Towards Understanding of Epidemics, Beliefs, and Behaviors
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Two-Stage Modeling for Dynamic Survival Prediction from Longitudinal Data
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Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
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Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition
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Video CLIP Model for Multi-View Echocardiography Interpretation
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Volatility-Aware Masking Improves Performance and Efficiency of Pretrained EHR Foundation Models
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Wavelet-Based Masked Multiscale Reconstruction for PPG Foundation Models