@inproceedings{
thompson2023large,
title={Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping},
author={Will Thompson and David Vidmar and Jessica De Freitas and Gabriel Altay and Kabir Manghnani and Andrew Nelsen and Kellie Morland and John Pfeifer and Brandon Fornwalt and RuiJun Chen and Martin Stumpe and Riccardo Miotto},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=Ujqjn2q9Gi}
}
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.
Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping
Will Thompson David Michael Vidmar Jessica Karina De Freitas Gabriel Altay Kabir Manghnani Andrew Nelsen Kellie Morland John Pfeifer Brandon Kenneth Fornwalt RuiJun Chen Martin Stumpe Riccardo Miotto