基于机器学习的预后建模,针对急性心力衰竭患者,在重症监护病房中接受富罗塞米德治疗
Tadashi Kamio1, Masaru Ikegami2, Yoshihito Machida2
1Division of Critical Care, Shonan Kamakura General Hospital, Kanagawa, Japan.
Digital health
|August 14, 2023
概括
机器学习模型准确地预测了接受罗塞米德治疗的急性心力衰竭患者的结果. 不同的模型最好预测死亡率,置换疗法和机械通风需求.
科学领域:
- 心脏病学 心脏病学
- 密集护理医学 密集护理医学
- 机器学习 机器学习
背景情况:
- 急性心力衰竭 (AHF) 是一种需要重症监护的危急病.
- 罗塞米德是对AHF的常见治疗方法,但患者的治疗结果各不相同.
- 预测临床结果对于及时干预至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测治疗罗塞米德的AHF患者的关键结果.
- 评估模型在预测住院死亡率,急性置换疗法 (RRT) 开始和机械通风 (MV) 开始方面的准确性.
主要方法:
- 利用来自日本一家连锁医院的广泛数据库.
- 使用静态和动态患者特征构建和训练ML模型.
- 使用接收器操作特征曲线 (AUC) 下面的面积来评估模型性能.
主要成果:
- ML模型在预测住院死亡率 (AUC=0.73),RRT启动 (AUC=0.70) 和MV启动 (AUC=0.73) 方面表现出很好的准确性.
- 线性支向量机器模型在预测死亡率和MV方面表现出色.
- 多层神经网络显示了预测RRT启动的最高准确性.
结论:
- ML模型在预测接受罗西米德的AHF患者的临床结果方面表现有希望.
- 最佳的ML模型因预测的具体结果而异.
- 这些发现可以帮助个性化治疗策略和集中护理机构的资源配置.
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