Zero-shot medical event prediction using a generative pretrained transformer on electronic health records.

ABSTRACT:

Objectives

Longitudinal data in electronic health records (EHRs) represent an individual's clinical history through a sequence of codified concepts, including diagnoses, procedures, medications, and laboratory tests. Generative pretrained transformers (GPT) can leverage this data to predict future events. While fine-tuning of these models can enhance task-specific performance, it becomes costly when applied to many clinical prediction tasks. In contrast, a pretrained foundation model can be used in zero-shot forecasting setting, offering a scalable alternative to fine-tuning separate models for each outcome.

Materials and methods

This study presents the first comprehensive analysis of zero-shot forecasting with GPT-based foundational models in EHRs, introducing a novel pipel

SUBMITTER: Redekop E 

PROVIDER: S-EPMC12646381 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature