Large language models accurately identify immunosuppression in intensive care unit patients.

ABSTRACT:

Objective

Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis codes and medication orders and (2) NLP approaches applied to unstructured notes.

Materials and methods

We used hospital admission notes from a primary cohort of 827 intensive care unit (ICU) pat

SUBMITTER: Guggilla V 

PROVIDER: S-EPMC12490808 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature