Large Language Models with Retrieval-Augmented Generation for Zero-Shot
  Disease Phenotyping
Will E. Thompson David M. Vidmar Jessica K. De Freitas John M. Pfeifer Brandon K. Fornwalt Ruijun Chen Gabriel Altay Kabir Manghnani Andrew C. Nelsen Kellie Morland Martin C. Stumpe Riccardo Miotto
Identifying disease phenotypes from electronic health records (EHRs) is
critical for numerous secondary uses. Manually encoding physician knowledge
into rules is particularly challenging for rare diseases due to inadequate EHR
coding, necessitating review of clinical notes. Large language models (LLMs)
offer promise in text understanding but may not efficiently handle real-world
clinical documentation. We propose a zero-shot LLM-based method enriched by
retrieval-augmented generation and MapReduce, which pre-identifies
disease-related text snippets to be used in parallel as queries for the LLM to
establish diagnosis. We show that this method as applied to pulmonary
hypertension (PH), a rare disease characterized by elevated arterial pressures
in the lungs, significantly outperforms physician logic rules ($F_1$ score of
0.62 vs. 0.75). This method has the potential to enhance rare disease cohort
identification, expanding the scope of robust clinical research and care gap
identification.