Foundation Model of Electronic Medical Records for Adaptive Risk Estimation (2502.06124v3)

Abstract: The U.S. allocates nearly 18% of its GDP to healthcare but experiences lower life expectancy and higher preventable death rates compared to other high-income nations. Hospitals struggle to predict critical outcomes such as mortality, ICU admission, and prolonged hospital stays. Traditional early warning systems, like NEWS and MEWS, rely on static variables and fixed thresholds, limiting their adaptability, accuracy, and personalization. We developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines (PHTs) from EHRs and uses transformer-based architectures to predict future PHTs. The Adaptive Risk Estimation System (ARES) leverages ETHOS to compute dynamic, personalized risk probabilities for clinician-defined critical events. ARES also features a personalized explainability module highlighting key clinical factors influencing risk estimates. We evaluated ARES on the MIMIC-IV v2.2 dataset in emergency department settings, benchmarking its performance against traditional early warning systems and machine learning models. From 299,721 unique patients, 285,622 PHTs (60% with hospital admissions) were processed, comprising over 357 million tokens. ETHOS outperformed benchmark models in predicting hospital admissions, ICU admissions, and prolonged stays, achieving superior AUC scores. Its risk estimates were robust across demographic subgroups, with calibration curves confirming model reliability. The explainability module provided valuable insights into patient-specific risk factors. ARES, powered by ETHOS, advances predictive healthcare AI by delivering dynamic, real-time, personalized risk estimation with patient-specific explainability. Its adaptability and accuracy offer a transformative tool for clinical decision-making, potentially improving patient outcomes and resource allocation.