Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records

Explore how large language models can be applied to extract psychiatric phenotypes from electronic health records in this 37-minute conference talk from the Computational Genomics Summer Institute. Learn about the methodologies and techniques for leveraging natural language processing and machine learning approaches to identify and categorize psychiatric conditions from clinical documentation. Discover the challenges and opportunities in processing unstructured medical text data, understand the validation processes for phenotype extraction accuracy, and examine real-world applications in psychiatric research and clinical genomics. Gain insights into the intersection of computational linguistics, healthcare informatics, and psychiatric research, including discussion of data privacy considerations, model performance metrics, and the potential impact on precision medicine approaches in mental health.