arXiv Machine Learning
Jun 24
HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning
arXiv:2509. 24118v2 Announce Type: replace Abstract: Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare.
Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture
arXiv:2606. 28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts.
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arXiv Machine Learning
Aug 10
MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
arXiv AI
Jun 3
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).
arXiv Machine Learning
Aug 4
EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records
arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.
arXiv Machine Learning
Jun 8
One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
arXiv Machine Learning
Aug 4
xMICD: Explainable Representation of Multiple ICD Codes
arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.
arXiv AI
Jul 8
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
arXiv:2607. 06163v1 Announce Type: cross Abstract: Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks.
arXiv Machine Learning
Jul 20
LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.
arXiv AI
Jun 10
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602. 12542v2 Announce Type: replace-cross Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions.
arXiv Machine Learning
Jun 10
Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
arXiv:2604. 07085v2 Announce Type: replace Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making.
arXiv Machine Learning
Jun 24
PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models
arXiv:2606. 24102v1 Announce Type: cross Abstract: Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values.
arXiv Machine Learning
Jul 27
Pretraining EHR Foundation Models with Patient-Aware Sampling
arXiv:2607. 22114v1 Announce Type: new Abstract: Autoregressive foundation models for electronic health records (EHRs) typically inherit pretraining methods from language modeling, where patient trajectories are concatenated into a single token stream and windows are sampled from that stream.