使用电子健康记录开发胃癌风险预测模型
Michelle Kang Kim1, Carol Rouphael1, Sarah Wehbe1
1Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio.
Gastro hep advances
|September 17, 2024
概括
使用电子健康记录的新物流回归模型可以识别患非心脏胃癌 (NCGC) 高风险的个体. 这种工具可以针对这种致命疾病进行有针对性的查.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 胃癌 (GC) 对全球健康造成重大负担,导致高发病率和死亡率.
- 由于其发病率和流行率较低,美国对GC的查具有挑战性.
- 开发风险预测算法对于有针对性的GC查策略至关重要.
研究的目的:
- 评估物流回归模型的可行性和性能.
- 使用电子健康记录 (EHR) 识别非心脏胃癌 (NCGC) 风险较高的个体.
主要方法:
- 使用614名被诊断患有NCGC的患者 (年龄40-80岁) 的EHR数据开发了一个后勤回归模型.
- 没有NCGC的对照被随机选择,比例为1:10.
- 多重归算处理了丢失的数据,后勤回归估计了NCGC概率. 模型歧视是使用0.632估计器进行评估的.
主要成果:
- 该模型表现出强大的性能,估计值为0.632的估计值为0.731.
- 增加NCGC概率的因素包括年龄,男性性别,黑人或亚洲种族,吸烟,贫血和恶性贫血.
结论:
- 基于EHR的后勤回归模型是可行的,并且在估计NCGC概率方面表现良好.
- 进一步的研究将完善和验证这种模型,用于识别NCGC查的高风险个人.
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