Large language models outperform traditional structured data-based approaches in identifying immunosuppressed patients
Journal:
medRxiv
Published Date:
Jan 1, 2025
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.
