Predictive Modeling and Deep Phenotyping of Obstructive Sleep Apnea and Associated Comorbidities through Natural Language Processing and Large Language Models

Abstract

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder associated with serious health conditions. This project utilized large language models (LLMs) to develop lexicons for OSA sub-phenotypes. Our study found that LLMs can identify informative lexicons for OSA subphenotyping in simple patient cohorts, achieving wAUC scores of 0.9 or slightly higher. Among the six models studied, BioClinical BERT and BlueBERT outperformed the rest. Additionally, the developed lexicons exhibited some utility in predicting mortality risk (wAUC score of 0.86) and hospital readmission (wAUC score of 0.72). This work demonstrates the potential benefits of incorporating LLMs into healthcare.