机器学习算法用于预测医院外心脏骤停发病率高的日子
Kaoru Shimada-Sammori1, Tadanaga Shimada1, Rie E Miura1,2
1Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan.
Scientific reports
|June 19, 2023
概括
机器学习使用天气数据准确预测出院心脏骤停 (OHCA) 事件. 前一天 前一天
科学领域:
- 环境健康 环境健康
- 数据科学在医学中的数据科学
- 公共卫生监督 公共卫生监督
背景情况:
- 改善非医院心脏骤停 (OHCA) 患者的治疗结果是关键的公共卫生目标.
- 对OHCA事件的预测模型可以实现及时干预和资源分配.
- 环境因素对OHCA发生率的影响需要进一步调查.
研究的目的:
- 开发和评估用于预测OHCA发生率的机器学习算法.
- 评估OHCA预测模型中气象和时间数据的有用性.
- 确定影响OHCA事件的关键环境和时间特征.
主要方法:
- 利用基于日本人口的OHCA数据库和相关的天气信息.
- 训练并测试了8个机器学习算法,包括XGBoost,以预测高频率的OHCA日.
- 采用沙普利添加式分析 (SHAP) 来确定特征的重要性,重点关注气象 (温度,湿度,气压) 和时间因素.
主要成果:
- XGBoost在训练队列中显示出高预测准确度,接收器操作曲线 (AUROC) 下的面积为0.906,在测试队列中为0.862-0.923.
- 该模型准确地预测了高发病率OHCA日,定义为每天超过75百分点的事件.
- SHAP分析显示",前一天的平均温度"是最重要的预测因素.
结论:
- 整合气象和时间数据的机器学习算法可以准确预测OHCA事件.
- 环境因素,特别是前一天的温度,在OHCA发生中起着至关重要的作用.
- 这些预测模型有潜力通过主动策略提高OHCA患者的治疗结果.
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