A GPT-based EHR modeling system for unsupervised novel disease detection.

ABSTRACT:

Objective

To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks.

Methods

Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent Large Language Models (LLMs), were leveraged to capture the dynamic evolution of the patient's clinical variables and compute an Out-Of-Distr

SUBMITTER: Hao B 

PROVIDER: S-EPMC12067473 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature