A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes

High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a large language model (LLM) incorporating generative AI, a deep learning (DL) approach utilizing span categorization, and a machine learning (ML) approach with word embeddings. The LLM approach that implemented GPT-4 demonstrated superior performance, suggesting that large language models are poised to become the preferred method for high-throughput phenotyping ofphysician notes.