Longitudinal EHR Modeling

EHR: Electronic Health Record

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16 papers in the last four weeks, up 433% on the four weeks before. 0.2% of all new papers.

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Latest papers 99

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FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
May 27, 2026Jun Bai, Ziyang Song, Yue LiSynthetic Data Generation Conditional Variational Autoencoder
McGill University Mila - Quebec AI Institute Montreal, Canada · Ohio University Athens, United States
GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease
May 27, 2026Leo Y. Li-Han, Ellen L. Larson, Elizabeth B. Habermann +2Temporal GNNs Graph Representation Learning
Department of Surgery, Mayo Clinic, Rochester, MN, USA · Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA · Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN, USA +1
Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
May 24, 2026Abdurrahim Yilmaz, Ayşe Esra Koku Aksu, Duygu Yamen +5Privacy-Preserving Language Models Healthcare
Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London · Department of Dermatology and Venereology, Istanbul Research and Training Hospital · Department of Dermatology and Venereology, Istanbul Medeniyet University +1
Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
May 21, 2026Niklas Raehse, Luregn J. Schlapbach, Daphné ChopardClinical Prediction Longitudinal EHR Modeling
Department of Intensive Care and Neonatology and Children’s Research Center, University of Zurich, University Children’s Hospital Zurich, Zurich, Switzerland · Department of Computer Science, ETH Zurich, Zurich, Switzerland
ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data
May 21, 2026Jiangyuan Wang, Xuyong Chen, Junwei He +3Latent Dynamics Modeling Healthcare
Beijing KidneyTec Medical Technology Co., Ltd., Beijing, China
Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series
May 19, 2026Kwanhyung Lee, Juhwan Choi, Jongheon Kim +5Irregular Time-Series Modeling Healthcare
Kim Jaechul Graduate School of AI, KAIST · AITRICS
Learning Normal Representations for Blood Biomarkers
May 18, 2026Aashna P. Shah, Michelle M. Li, Yash Lal +9Healthcare Longitudinal EHR Modeling
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA · Department of Systems Biology, Harvard Medical School, Boston, MA, USA · The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, USA and Israel +5
From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
May 16, 2026Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari +10Causal Effect Estimation Off-Policy Evaluation
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China. · Medin.ai, Beijing 100871, China. · School of Software & Microelectronics, Peking University, Beijing 100871, China. +8
Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
May 14, 2026Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim +1Multimodal RAG Healthcare
Division of Intramural Research National Library of Medicine, National Institutes of Health Bethesda, MD, USA
COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion
May 14, 2026Zihan Deng, Xiaozhen Zhong, Chuanzhi XuHealthcare Clinical Decision Support
School of Computing and Data Science, The University of Hong Kong · 2Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China · School of Computer Science, The University of Sydney
DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
May 14, 2026Yunying Zhu, Andrew R Weckstein, Kueiyu Joshua Lin +1Healthcare Clinical Prediction
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA · Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA +2
Training Large Language Models to Predict Clinical Events
May 12, 2026Benjamin Turtel, Paul Wilczewski, Kris SkotheimHealthcare Language Model Calibration
Lightning Rod Labs
EHR-RAGp: Prototype-Guided Retrieval of Longitudinal Electronic Health Records for Clinical Prediction Models
May 12, 2026Saeed Shurrab, Mariam Al-Omari, Dana El Samad +1Long-Context Modeling Clinical Prediction
New York University · New York University Abu Dhabi
From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
May 12, 2026Mingcheng Zhu, Zhiyao Luo, Yu Liu +1LLM Compression Clinical Prediction
Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
May 11, 2026Yixuan Yang, Mehak Arora, Ryan Zhang +10Self-Supervised Pre-Training Healthcare
Duke University, Durham, NC, USA
ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
May 11, 2026Jinwoong Kim, Sangjin ParkIrregular Time-Series Modeling Clinical Outcome Prediction
Department of Industrial Data Engineering Hanyang University Seoul, Republic of Korea
Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning
May 10, 2026Ziquan Wei, Tingting Dan, Guorong WuHealthcare Digital Twins
Department of Computer Science 2Department of Psychiatry University of North Carolina at Chapel Hill
Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation
May 6, 2026Olivia Jullian Parra, Sara Zoccheddu, David Catalan Cerezo +7Causal Effect Estimation Normalizing Flows
Department of Mathematical Modeling and Machine Learning University of Zürich Winterthurerstrasse 190, Zürich 8057 · Department of Mathematics ETH Zürich Rämistrasse 101, Zürich 8092 · Epidemiology, Biostatistics & Prevention Institute (EBPI), University of Zürich Hirschengraben 84, Zürich 8001 +2
From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways
May 5, 2026Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile +1Predictive Process Monitoring Process Mining
Department of Informatics, University of Bari Aldo Moro, Bari, Italy · Department of Engineering, University of Sannio, Benevento, Italy · Department of Economy and Law, UnitelmaSapienza University, Rome, Italy
Temporal Data Requirement for Predicting Unplanned Hospital Readmissions
May 1, 2026Ramin Mohammadi, Vahab vahdat, Sarthak Jain +3Clinical Outcome Prediction Clinical Prediction
Northeastern University, Boston, MA, US · Partners Healthcare Connected Health Innovation, Boston, MA, USA · MGH Institute for Technology Assessment, Boston, MA, USA +2
Deep Kernel Learning for Stratifying Glaucoma Trajectories
May 1, 2026Bruce Rushing, Angela Danquah, Alireza Namazi +2Clinical Outcome Prediction Kernel Methods
Research Computing, University of Virginia · School of Data Science, University of Virginia · School of Medicine, University of Virginia
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Apr 30, 2026Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2Irregular Time-Series Modeling Healthcare
Universit´e Paris Cit´e and Universit´e Sorbonne Paris Nord, INRIA, Inserm, HEKA, Paris, 75015, France. · Department of Medical Informatics, Assistance Publique Hˆopitaux de Paris (AP-HP), Georges Pompidou European Hospital, Paris, France. · SIERRA, Inria Paris, Paris, 75015, France. +2
Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records
Apr 27, 2026Kalyani P. Pande, Evan Yang, Bryan Zhu +3Causal Effect Estimation Healthcare
Stony Brook University, Stony Brook, NY, USA
A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency
Apr 24, 2026Nanae Aratake, Taisei Tosaki, Yuji Okamoto +5Self-Supervised Pre-Training Clinical Outcome Prediction
1Graduate School of Medicine, Kyoto University, Kyoto, Japan. · 2Medical Data Vision Co., Ltd., Tokyo, Japan. · 3IQVIA Solutions Japan G.K., Tokyo, Japan. +1
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Apr 23, 2026Zihan Liang, Ziwen Pan, Ruoxuan XiongClinical Outcome Prediction Offline RL
Emory University, Atlanta, USA
Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury
Apr 22, 2026Weizhi Nie, Haolin ChenCausal Interpretability Longitudinal EHR Modeling
School of Electrical and Information Engineering, Tianjin University
Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
Apr 20, 2026Andrew Wang, Ellie Pavlick, Ritambhara SinghMissing-Modality Learning Multimodal Contrastive Learning
Brown University
A multimodal and temporal foundation model for virtual patient representations at healthcare system scale
Apr 20, 2026Andrew Zhang, Tong Ding, Sophia J. Wagner +8Multimodal IR Representation Learning
Department of Pathology, Mass General Brigham, Harvard Medical School, Boston, MA · Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA · Data Science Program, Dana-Farber Cancer Institute, Boston, MA +5
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
Apr 20, 2026Arya Hadizadeh Moghaddam, Drew Ross, Mohsen Nayebi Kerdabadi +2Representation Learning Prompt Tuning
University of Kansas, USA
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
Apr 18, 2026Zhongyuan Liang, Junhyung Jo, Hyang-Jung Lee +2Contrastive Learning Cross-Modal Knowledge Distillation
Computational Precision Health UC Berkeley, UCSF · Samsung Advanced Institute of Technology (SAIT) · Computational Precision Health UC Berkeley, UCSF[cs.LG]

May 27, 2026, cs.LGJ/K move · Enter open · S save

FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation

Jun Bai, Ziyang Song, Yue Li

McGill University Mila - Quebec AI Institute Montreal, Canada · Ohio University Athens, United States

Synthetic Data Generation Conditional Variational Autoencoder Longitudinal EHR Modeling Electronic Health Records Heterogeneous Federated Learning

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Synthetic Electronic Health Record (EHR) generation provides a promising avenue for data augmentation and cross-hospital modeling in privacy-constrained healthcare settings. However, most existing EHR generative models are centralized and require pooling data across hospitals, which is often infeasible when real-world data sharing is restricted. While federated EHR generation offers a natural solution, direct federated modeling often collapses or diverges due to the high dimensionality, sparsity, and cross-hospital heterogeneity of EHR data. In this work, we propose FedEHR-Gen, the first federated framework for synthetic time-series EHR generation across distributed hospitals. FedEHR-Gen uses a two-stage learning paradigm. First, we introduce a federated autoencoder that projects high-dimensional and sparse EHR features onto a compact latent space. To ensure semantic consistency across hospitals, we develop a layer-wise matching aggregation mechanism that aligns local encoders into a unified global latent space. Second, operating on this aligned latent space, we train a federated temporal conditional variational autoencoder (TCVAE) with distribution-aware aggregation, enabling stable temporal generative modeling under severe cross-hospital heterogeneity. Extensive experiments on the eICU and MIMIC-III datasets demonstrate that FedEHR-Gen achieves generation fidelity, downstream utility, and privacy risk comparable to centralized training, while consistently outperforming the standard federated baseline.

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