CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records

• URL: http://arxiv.org/abs/2509.03643v2
• Date: Fri, 05 Sep 2025 12:40:38 GMT
• Title: CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records
• Authors: Chao Pang, Jiheum Park, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve, Krishna S. Kalluri, Shalmali Joshi, Noémie Elhadad, Karthik Natarajan,
• Abstract summary: CEHR-XGPT is a general-purpose foundation model for EHR data.<n>It unifies three essential capabilities - feature representation, zero-shot prediction, and synthetic data generation.<n>It demonstrates strong performance across all three tasks and generalizes effectively to external datasets.
• Score: 9.583050730170557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electronic Health Records (EHRs) provide a rich, longitudinal view of patient health and hold significant potential for advancing clinical decision support, risk prediction, and data-driven healthcare research. However, most artificial intelligence (AI) models for EHRs are designed for narrow, single-purpose tasks, limiting their generalizability and utility in real-world settings. Here, we present CEHR-XGPT, a general-purpose foundation model for EHR data that unifies three essential capabilities - feature representation, zero-shot prediction, and synthetic data generation - within a single architecture. To support temporal reasoning over clinical sequences, CEHR-XGPT incorporates a novel time-token-based learning framework that explicitly encodes patients' dynamic timelines into the model structure. CEHR-XGPT demonstrates strong performance across all three tasks and generalizes effectively to external datasets through vocabulary expansion and fine-tuning. Its versatility enables rapid model development, cohort discovery, and patient outcome forecasting without the need for task-specific retraining.

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