Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.

OBJECTIVE: Leveraging large language models, we identified an unregistered subpopulation of individuals with World Trade Center-related exposure and lung cancer who were not previously captured in registries, and assessed how this exposure impacted patients' disease course. METHODS: Associations between exposure type and smoking history, cancer stage, mutation status, disease progression, and survival were statistically analyzed. RESULTS: The highest proportion of never-smokers was observed among residents, compared with First Responders and Commuters (19% and 24%; P = 0.005). Residents had more than twice the risk of disease progression (Hazard Ratio (HR) = 2.14, P = 0.008) and an elevated risk of death (HR = 2.43, P = 0.03). Only EGFR mutations were significantly associated with exposure type ( P = 0.01). CONCLUSIONS: This work highlights that large language model can capture a greater population of World Trade Center survivors, including genetics.