{"ID":2875144,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03643","arxiv_id":"2509.03643","title":"CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records","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.","short_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, lim...","url_abs":"https://arxiv.org/abs/2509.03643","url_pdf":"https://arxiv.org/pdf/2509.03643v2","authors":"[\"Chao Pang\",\"Jiheum Park\",\"Xinzhuo Jiang\",\"Nishanth Parameshwar Pavinkurve\",\"Krishna S. Kalluri\",\"Shalmali Joshi\",\"Noémie Elhadad\",\"Karthik Natarajan\"]","published":"2025-09-03T18:50:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}