Large language models outperform traditional structured data-based approaches in identifying immunosuppressed patients.

ABSTRACT: Identifying immunosuppressed patients using structured data can be challenging. Large language models effectively extract structured concepts from unstructured clinical text. Here we show that GPT-4o outperforms traditional approaches in identifying immunosuppressive conditions and medication use by processing hospital admission notes. We also demonstrate the extensibility of our approach in an external dataset. Cost-effective models like GPT-4o mini and Llama 3.1 also perform well, but not as well as GPT-4o.

SUBMITTER: Guggilla V 

PROVIDER: S-EPMC11759841 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature