Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.
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- Record sourced from PubMed, PMID 42168810.
- Also identified by DOI 10.1097/JOM.0000000000003760.
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Abstract
Leveraging large language models (LLM), 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. Associations between exposure type and smoking history, cancer stage, mutation status, disease progression, and survival were statistically analyzed. The highest proportion of never-smokers was observed among residents, compared to first responders and commuters (19% and 24%; p = 0.005). Residents had more than twice the risk of disease progression (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). This work highlights that LLM can capture a greater population of WTC survivors, including genetics.