Longitudinal EHR Modeling

EHR: Electronic Health Record

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Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models
Apr 16, 2026Sicong Chang, Yidan Shen, Justina Varghese +7Medical Diagnosis Clinical Prediction
Electrical and Computer Engineering University of Houston Houston, USA · Otolaryngology Houston Methodist Hospital Houston, USA · Information Science Technology, University of Houston & Otolaryngology, Houston Methodist Hospital Houston, USA
A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs
Apr 6, 2026Bohao Li, Tao Zou, Junchen Ye +2Electronic Health Records Clinical Risk Prediction
Beihang University, Beijing, China · The Hong Kong Polytechnic University, Hong Kong, China
Capture Timing-Attention of Events in Clinical Time Series
Feb 11, 2026Jia Li, Yu Hou, Rui ZhangEvent Time Prediction Healthcare
Department of Surgery; Department of Computer Science, U of M Minneapolis, MN, USA
MedBeads: An AI-Native Clinical Context Graph Built from Immutable Beads and Reconstructable Clinical Links
Feb 1, 2026Takahito NakajimaKG Construction LLM Grounding
Diagnostic Imaging and Interventional Radiology, Institute of Medicine, University of Tsukuba · Center for Cyber Medicine Research, University of Tsukuba
Chronic Kidney Disease Prognosis Prediction Using Transformer
Nov 4, 2025Yohan Lee, Dong Gyun Kang, SeHoon Park +2Clinical Outcome Prediction Longitudinal EHR Modeling
Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, South Korea · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA · Department of Nephrology, Seoul National University Hospital, Seoul, South Korea +1
HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning
Sep 28, 2025Md Mozaharul Mottalib, Thao-Ly T. Phan, Rahmatollah BeheshtiRepresentation Learning Longitudinal EHR Modeling
University of Delaware Newark, Delaware, USA · Thomas Jefferson University & Nemours Children’s Health Wilmington, Delaware, USA
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Mar 18, 2024Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed +4Healthcare Oncology
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization
Date pendingInhyeok Lee, Luke Solo, Michael C. Burkhart +5Healthcare Predictive Representation Learning
University of Chicago
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Date pendingXinrui He, Mengting Ai, Junting Wang +2Time Series Imputation Incomplete Data Imputation
University of Illinois Urbana-Champaign Champaign, IL, USA · Mayo Clinic Scottsdale, AZ, USA

Apr 16, 2026, cs.LGJ/K move · Enter open · S save

Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models

Sicong Chang, Yidan Shen, Justina Varghese, Akshay R Prabhakar+6

Electrical and Computer Engineering University of Houston Houston, USA · Otolaryngology Houston Methodist Hospital Houston, USA · Information Science Technology, University of Houston & Otolaryngology, Houston Methodist Hospital Houston, USA

Medical Diagnosis Clinical Prediction Longitudinal EHR Modeling Clinical Risk Prediction

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Chronic rhinosinusitis (CRS) is a common heterogeneous inflammatory disorder that causes substantial morbidity and healthcare costs. CRS is difficult to identify early from routine encounters, as symptom presentations overlap with common conditions such as allergic rhinitis, and heterogeneous phenotypes further obscure risk patterns. Prior predictive studies often rely on single-institutional cohorts , which reduce population-level generalizability. To overcome this, we leveraged nationwide longitudinal EHR data from the \textit{All of Us} Research Program to predict CRS diagnosis using two years of pre-diagnostic history. To address extreme feature sparsity and dimensionality in coded EHR data, we implemented a hybrid feature-selection pipeline that combines prevalence-based statistical screening with model-based importance ranking, compressing approximately 110,000 candidate codes into 100 interpretable features. To capture demographic heterogeneity, we trained demographic stratified models across six adult sex and life-stage subgroups with subgroup-specific hyperparameter tuning. Our framework achieved an overall AUC of 0.8461, improving discrimination by 0.0168 over the best baseline. These results demonstrate that routinely collected EHR data may support population-representative CRS risk stratification and inform earlier triage and referral prioritization in primary care.

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