Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.

ABSTRACT: The accurate detection of clinical phenotypes from electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal studies in psychiatry. Free-text clinical notes are an essential source of symptom-level information, particularly in psychiatry. However, the automated extraction of symptoms from clinical text remains challenging. Here, we tested 11 open-source generative large language models (LLMs) for their ability to detect 109 psychiatric phenotypes from clinical text, using annotated EHR notes from a psychiatric clinic in Colombia. The LLMs were evaluated both "out-of-the-box" and after fine-tuning, and compared against a traditional natural language processing (tNLP) method developed from the same data. We show that while base LLM performance was poor to

SUBMITTER: Frydman-Gani C 

PROVIDER: S-EPMC12363723 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature