Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
Journal:
arXiv
Published Date:
Feb 10, 2025
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.
