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早期检测膀癌使用先进的功能工程和集群智能优化对EHRs的早期检测
IEEE transactions on bio-medical engineering
|January 26, 2026
概括
机器学习模型现在可以使用电子健康记录 (EHR) 选膀癌 (BC). PRECISE-AGZ管道确定了新的风险因素,使得在初级保健机构的早期检测成为可能.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 膀癌 (BC) 的症状往往模仿良性疾病,使早期诊断复杂化.
- 在一般人群中,膀癌的常规查缺乏.
- 电子健康记录 (EHR) 为开发查工具提供了一个潜在的数据源.
研究的目的:
- 开发和验证基于机器学习 (ML) 的查管道,用于早期检测膀癌 (BC).
- 使用来自初级保健机构的EHR数据进行BC查.
- 通过ML分析识别BC风险的新型预测因子.
主要方法:
- 一个多中心病例控制队列 (n=64,884) 用于模型培训和测试 (1995-2018).
- 在独立队列 (n=4,569) (2019-2020) 上进行了前性验证.
- 在Parsimony驱动的REweighting for Calibrated Input-based Screening for Early detection-Adjustable Grey Zone (PRECISE-AGZ) 管道中,确定了具有影响力的特征,并开发了一个校准后勤回归模型.
主要成果:
- 该模型确定了38个特征,在测试组中达到0.789的AUC.
- 新的预测因素包括神经系统疾病 (如帕金森病) 和药物 (如塔莫西芬).
- 低风险类别 (预测概率≤0.55) 实现了85.2%的灵敏度,0.799的F1得分和34.5%的查人口覆盖率.
结论:
- PRECISE-AGZ管道有效地从EHR中提取临床信号,用于早期发现BC.
- 这种方法显示出实施基于人口的膀癌查的巨大潜力.
- 该研究强调了ML在识别早期疾病检测微妙指标方面的实用性.
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