Predicting Early-Onset Colorectal Cancer with Large Language Models.
Publication Title
AMIA ... Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium
Document Type
Article
Publication Date
1-1-2024
Keywords
Humans; Colorectal Neoplasms; Machine Learning; Retrospective Studies; Age of Onset; United States; Female; Sensitivity and Specificity; Middle Aged; Male; Early Detection of Cancer; Natural Language Processing; Adult; Large Language Models; washington; swedish; artifical intelligence
Abstract
The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity.
Area of Special Interest
Cancer
Area of Special Interest
Digestive Health
Specialty/Research Institute
Oncology
Specialty/Research Institute
Gastroenterology
