{"total":67,"rows":[{"id":"2502.06124","title":"Foundation Model of Electronic Medical Records for Adaptive Risk Estimation","created":"2025-02-10","cited_by_count":19,"area":null},{"id":"2502.04397","title":"Multimodal Medical Code Tokenizer","created":"2025-02-06","cited_by_count":14,"area":null},{"id":"2505.22964","title":"Exploring Scaling Laws for EHR Foundation Models","created":"2025-05-29","cited_by_count":12,"area":null},{"id":"2504.10422","title":"Foundation models for electronic health records: representation dynamics and transferability","created":"2025-04-14","cited_by_count":11,"area":null},{"id":"2505.16941","title":"FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records","created":"2025-05-22","cited_by_count":6,"area":null},{"id":"2603.15644","title":"Tokenization Tradeoffs in Structured EHR Foundation Models","created":"2026-03-03","cited_by_count":6,"area":null},{"id":"2502.14259","title":"LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records","created":"2025-02-20","cited_by_count":5,"area":null},{"id":"2510.12950","title":"An Investigation of Memorization Risk in Healthcare Foundation Models","created":"2025-10-14","cited_by_count":5,"area":null},{"id":"2602.00541","title":"One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models","created":"2026-01-31","cited_by_count":5,"area":null},{"id":"2502.20719","title":"Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer","created":"2025-02-28","cited_by_count":4,"area":null},{"id":"2504.08329","title":"MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models","created":"2025-04-11","cited_by_count":4,"area":null},{"id":"2509.03643","title":"CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records","created":"2025-09-03","cited_by_count":4,"area":null},{"id":"2510.09159","title":"Cross-Representation Benchmarking in Time-Series Electronic Health Records for Clinical Outcome Prediction","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2604.01841","title":"Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints","created":"2026-04-02","cited_by_count":4,"area":null},{"id":"2411.00066","title":"Interpretable Next-token Prediction via the Generalized Induction Head","created":"2024-10-31","cited_by_count":3,"area":null},{"id":"2506.04831","title":"EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2507.00574","title":"Foundation Models for Clinical Records at Health System Scale","created":"2025-07-01","cited_by_count":3,"area":null},{"id":"2507.22798","title":"Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models","created":"2025-07-30","cited_by_count":3,"area":null},{"id":"2603.14003","title":"The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records","created":"2026-03-14","cited_by_count":3,"area":null},{"id":"2603.24562","title":"Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction","created":"2026-03-25","cited_by_count":3,"area":null},{"id":"2501.02648","title":"Representation Learning of Lab Values via Masked AutoEncoders","created":"2025-01-05","cited_by_count":2,"area":null},{"id":"2602.03730","title":"Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2603.11679","title":"LLMs can construct powerful representations and streamline sample-efficient supervised learning","created":"2026-03-12","cited_by_count":2,"area":null},{"id":"2606.24102","title":"PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models","created":"2026-06-23","cited_by_count":2,"area":null},{"id":"2607.08793","title":"EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins","created":"2026-07-07","cited_by_count":2,"area":null},{"id":"2410.02133","title":"TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis","created":"2024-10-03","cited_by_count":1,"area":null},{"id":"2504.11344","title":"Interpretable Hybrid-Rule Temporal Point Processes","created":"2025-04-15","cited_by_count":1,"area":null},{"id":"2505.21680","title":"multivariateGPT: a decoder-only transformer for multivariate categorical and numeric data","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2506.23358","title":"Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment","created":"2025-06-29","cited_by_count":1,"area":null},{"id":"2510.22878","title":"Limits of Generative Pre-Training in Structured EMR Trajectories with Irregular Sampling","created":"2025-10-27","cited_by_count":1,"area":null},{"id":"2510.23639","title":"Integrating Genomics into Multimodal EHR Foundation Models","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2603.06720","title":"From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2604.18753","title":"Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2605.02740","title":"Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2608.07705","title":"Protecting patient privacy in clinical foundation models: Technical and legal perspectives","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2503.03724","title":"Deep Causal Behavioral Policy Learning: Applications to Healthcare","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2506.00209","title":"Scaling Electronic Health Record Foundation Models for Population Health Management","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2506.13790","title":"The NordDRG AI Benchmark for Large Language Models","created":"2025-06-11","cited_by_count":0,"area":null},{"id":"2510.01114","title":"PRISM-Consult: A Panel-of-Experts Architecture for Clinician-Aligned Diagnosis","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2510.14286","title":"Stable Prediction of Adverse Events in Medical Time-Series Data","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2511.02340","title":"Chronic Kidney Disease Prognosis Prediction Using Transformer","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2511.11293","title":"Toward Scalable Early Cancer Detection: Evaluating EHR-Based Predictive Models Against Traditional Screening Criteria","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2511.22199","title":"PULSE-ICU: A Pretrained Unified Long-Sequence Encoder for Multi-task Prediction in Intensive Care Units","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2512.05216","title":"Coefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2602.00520","title":"NEST: Nested Event Stream Transformer for Sequences of Multisets","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2602.09079","title":"Patient foundation model for risk stratification in low-risk overweight patients","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.15852","title":"Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2603.07900","title":"EveryQuery: A Promptable Foundation Model for Clinical Prediction Tasks over Electronic Health Records","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2604.02337","title":"Generating Counterfactual Patient Timelines from Real-World Data","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2604.16775","title":"Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2605.14004","title":"Conditional Attribute Estimation with Autoregressive Sequence Models","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.14227","title":"DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.11220","title":"LifeSentence: Language models can encode human life course trajectories from longitudinal panel data","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.15447","title":"Hierarchical Modeling of ICD Codes in EHR Foundation Models","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.01391","title":"How Should Transformers Encode Numeric Values in Electronic Health Records?","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.06163","title":"X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.15447","title":"LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2607.22114","title":"Pretraining EHR Foundation Models with Patient-Aware Sampling","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2607.22264","title":"Autoregressive EHR Foundation Models with Multimodal Inputs","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.02939","title":"Federated generative event models for tokenized electronic health records","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.16273","title":"Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.24688","title":"A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2608.26191","title":"Structured Evidence Routing for Incident Risk Prediction from Multimodal Longitudinal EHRs","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.12277","title":"Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.23333","title":"A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.31635","title":"What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2610.16878","title":"Sequence Composition in EHR Foundation Model Pretraining","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}