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机器学习算法用于预测癌症幸存者的心血管疾病

Sherry-Ann Brown^1,2,3, Michelle Z Fang⁴, Rodney Sparapani⁵

¹Department of Medicine Medical College of Wisconsin Milwaukee WI USA.

Journal of the American Heart Association

|December 11, 2025

概括

预测癌症幸存者的心血管疾病 (CVD) 风险至关重要. 机器学习模型在这个人群中显示出准确的CVD风险预测的前景.

科学领域:

心脏瘤学是一门专业.
机器学习在医学中的应用
生物统计学生物统计学

背景情况:

心血管疾病 (CVD) 是癌症幸存者死亡的主要原因.
在癌症幸存者中准确预测心血管疾病风险是一个重大的临床挑战.
机器学习 (ML) 为客观和精确的CVD风险评估提供了一个潜在的解决方案.

研究的目的:

评估各种ML算法和规范后勤回归的性能,以预测癌症幸存者的心血管疾病风险.
将已建立的ML技术的预测精度与较新的,更复杂的算法进行比较.
评估这些模型对识别患心血管疾病高风险的幸存者的有用性.

主要方法:

一项多中心研究涉及3835名癌症幸存者,在20年内收集了89个特征.
模型在随机和时间分割样本上进行训练,并在329名患者的单独队列上进行验证.
使用接收器操作特征曲线 (AUC) 下的面积来评估性能.

主要成果:

调节后勤回归实现的AUC为0.845 (心力衰竭),0.792 (冠状动脉疾病) 和0.806 (复合心血管疾病).
先进的ML模型,如贝叶斯增量回归树和随机森林,在心力衰竭预测方面表现相似.
调节后勤回归也有效地预测了新组合CVD (AUC 0.826).

结论:

规范后勤回归和先进的ML模型都表现出类似的,在癌症幸存者中对心血管疾病具有机构可转移的预测能力.
这些计算工具可以帮助风险分层和制定有针对性的预防策略.
纵向数据分析是通过预测建模推进心脏瘤学的关键.

关键词:

人工智能是一种人工智能.癌症幸存者癌症幸存者心脏毒性心脏毒性心脏瘤学心脏瘤学超声心电图 (Echocardiography) 是一种心声回声仪.机器学习是机器学习.

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