对心力衰竭患者基于机器学习的可解释的生存模型进行比较和使用
Tao Shi1, Jianping Yang2, Ningli Zhang3
1Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Digital health
|August 28, 2024
概括
随机生存森林 (rfsrc) 模型在预测心力衰竭 (HF) 患者的整体存活率 (OS) 方面优于考克斯比例危险回归 (coxph). 可解释的AI方法为HF患者提供了个性化的治疗见解.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 心力衰竭 (HF) 管理需要准确的预后和个性化的治疗策略.
- 可解释的机器学习模型对于在临床环境中理解复杂的生存数据至关重要.
研究的目的:
- 为了比较随机生存森林 (rfsrc) 和Cox比例危险回归 (coxph) 模型的性能,用于预测心力衰竭 (HF) 患者的整体存活率 (OS).
- 利用可解释的人工智能 (XAI) 技术,对高频生存模型进行全球和本地解释.
- 为了确定HF患者的OS的关键决定因素.
主要方法:
- 分析了一组1159名高血压患者的队列.
- 用C指数,综合C/D AUC和综合Brier评分来评估模型性能.
- 全球解释涉及时间依赖的变量重要性和部分依赖生存概况.
- 当地解释使用了SurvSHAP (t),SurvLIME图,以及其他类似的生存概况.
主要成果:
- 与coxph模型相比,rfsrc模型表现出更高的性能.
- 在HF患者中不良的OS的关键预测因素包括C反应蛋白,lg BNP,估计的淋巴膜过率,专蛋白,年龄和血液化物.
- XAI方法提供了队列层面的见解和针对患者的具体解释.
结论:
- rfsrc模型是比coxph更有效的工具,用于预测HF患者的OS.
- 可解释的人工智能通过为个人HF患者提供个性化治疗建议来提高生存模型的临床实用性.
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