Predicting Early-Onset Colorectal Cancer with Large Language Models

The incidence rate of early - onset colorectal cancer (EoCRC, age < 45) has increased every year, but this populanullon is younger than the recommended age established by nanullonal guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance w ith advanced large language models (LLM), using panullent condinullons, lab results, and observanullons within 6 months of panullent journey prior to the CRC diagnoses. The results demonstrated that the fine - tuned LLM achieved an average of 73% sensinullvity and 91% specificity. Introducnullon Colorectal cancer (CRC) is a significant public health concern, ranking as the second leading cause of cancer - related deaths and the 4th most common new cancer diagnosis in the U.S. in 2024. While CRC has historically been considered a disease of older adults, there has been an increase in colorectal cancer diagnosed in individuals under 50. Between 2011 and 2019, CRC incidence rates increased by 1.9% per year in people younger than 50 years. Furthermore, between 2012 and 2021, among individuals aged 20 to 49, the incidence of advanced - stage colorectal cancer increased by approximately 3% per year .