Clinical risk prediction using language models: benefits and considerations.

ABSTRACT:

Objective

The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks.

Methods

We propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes.

Results

SUBMITTER: Acharya A 

PROVIDER: S-EPMC11339498 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature