可解释的等级分类用于患者亚型和风险预测
Enrico Werner1, Jeffrey N Clark1, Alexander Hepburn1
1University of Bristol, Bristol BS1 5DD, UK.
Experimental biology and medicine (Maywood, N.J.)
|December 16, 2023
概括
机器学习使用常规医院数据识别患者亚型,优于国家早期预警分数2 (NEWS2) 预测患者病情恶化. 这种方法将计算分析与临床专业知识相结合,以改善患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床数据分析 临床数据分析
背景情况:
- 准确的患者分层对于有效的医院管理和预测临床恶化至关重要.
- 现有的评分系统,如国家早期预警评分2 (NEWS2),提供了一般的患者概述,但可能缺乏细节性.
- 机器学习的整合为更细微的患者亚型提供了潜力.
研究的目的:
- 开发和评估用于自动识别和临床解释医院患者亚型的机器学习管道.
- 将已识别的患者亚型的预测性能与已建立的NEWS2评分系统进行比较.
- 探索机器学习驱动的子类型和临床专业知识之间的协同作用.
主要方法:
- 利用从英国一家教学医院定期收集的医院数据 (2017-2021年).
- 采用代的,层次的集群来识别患者分层的关键特征.
- 应用可解释性技术用于临床解释亚型和训练结果预测模型的每个集群.
主要成果:
- 确定了具有临床意义的解释的不同患者亚型,并由临床医生验证.
- 患者亚型的结果预测模型显示,与NEWS2.2相比,患者病情恶化的预测优越.
- 通过可解释性技术和临床医生评估,展示了已识别的亚型的稳定性.
结论:
- 基于机器学习的患者亚型化可以显著提高患者病情恶化的预测.
- 将计算分析与临床专业知识相结合,为患者分层提供了一个强大的方法.
- 这种方法有望改善个性化患者管理和临床决策.
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