机器学习系统的开发,以预测与癌症相关的症状,在整个医疗保健系统中进行验证
Baijiang Yuan1,2, Muammar Kabir3, Jiang Chen He1,2,4
1Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
JCO clinical cancer informatics
|September 25, 2025
概括
机器学习模型可以使用电子健康记录预测癌症患者未来的症状恶化. 这项技术可以帮助个性化癌症护理和干预措施.
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:
- 癌症治疗通常会导致衰弱的症状,影响患者的生活质量.
- 预测症状恶化对于及时有效的患者管理至关重要.
研究的目的:
- 开发和验证用于预测癌症治疗患者未来症状恶化的机器学习 (ML) 系统.
- 评估在医疗保健系统中部署这些ML系统的可行性.
主要方法:
- 训练有素的ML系统可在30天内预测空气消化道癌症患者使用电子健康记录 (EHR) 数据的9种症状的症状恶化.
- 使用元分析技术在82个癌症中心内部和外部验证了表现最佳的模型.
- 主要的性能指标是接收器操作特征曲线 (AUROC) 下的面积.
主要成果:
- ML系统的AUROC为0.66至0.73,用于预测症状恶化.
- 高风险的治疗与未来的症状恶化和针对特定症状的急诊室访问有显著的相关性.
- 外部验证显示,各中心的表现一致,尽管一些症状具有显著的异质性.
结论:
- 机器学习模型可以有效地利用常规EHR数据预测癌症患者的未来症状.
- 这些预测能力可以为个性化干预提供信息,并改善患者护理.
- 考虑性能异质性对于成功的全系统部署至关重要.
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