用大模型分析医疗记录，提前预测年轻人群结直肠癌风险
Predicting Early-Onset Colorectal Cancer with Large Language Models
- 用10种机器学习模型对比，微调大语言模型表现最优
- 在诊断前6个月内数据下，灵敏度达73%，特异性91%
- 适合关注早发癌症筛查的临床医生和健康科技研究者
早发性结直肠癌（EoCRC，年龄<45岁）的发病率逐年上升，但该人群年龄低于国家指南推荐的筛查起始年龄。本文应用10种不同机器学习模型预测EoCRC，与先进大语言模型（LLM）进行性能比较，使用患者就诊前6个月内病史、检验结果及临床观察数据。从美国多个医疗系统中回顾性识别出1,953例结直肠癌患者。结果表明，微调后的大语言模型平均灵敏度为73%，特异性达91%。
原文摘要 · Abstract (English)
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.
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