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

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.