在预测心力衰竭结果的机器学习模型中,什么驱动性能?
Rom Gutman1, Doron Aronson2,3, Oren Caspi2,3
1William Davidson Faculty of Industrial Engineering and Management, Technion, Haifa, Israel.
European heart journal. Digital health
|June 2, 2023
概括
准确预测急性心力衰竭 (AHF) 预后更多地依赖于使用的患者数据的数量和类型,而不是特定的机器学习模型. 利用综合数据可以改善AHF患者的风险分层.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 急性心力衰竭 (AHF) 呈现出一个危急的关键时刻,预后不佳.
- 目前在医院出院时的风险分层工具对于量身定制的治疗是不够的.
- 机器学习为使用复杂的患者数据改进AHF风险预测提供了潜力.
研究的目的:
- 确定推动AHF预测模型成功的关键因素.
- 开发一个针对特定机构的基于人工智能的预测模型,用于实时临床决策支持.
主要方法:
- 分析了12年来10868名AHF患者的队列.
- 从入院到住院收集了372个共变量.
- 评估了七个机器学习模型,包括后勤回归,随机森林,Cox,XGBoost,NeuralNet和一个整体模型.
- 模型的性能根据预测方法和共变量类型/数量进行评估,以1年生存为主要结果.
主要成果:
- 大多数模型实现了>80%的预测准确度 (AUROC).
- 整体模型显示略高的性能 (81.2%的AUROC).
- 共同变量的数量和类型显著影响了预测的成功 (P < 0.001),多重共同变量超过了传统的临床变量 (80.4% vs 77.8% AUROC).
- 人口统计,实验室测试和行政数据提供了最显著的绩效增长.
结论:
- 预测建模方法的选择比用于AHF预后的共同变量的多重性和类型不那么关键.
- 结构化数据预处理和多重共变量的使用为AHF提供了准确的,特定于机构的风险预测.
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