构建和验证ICU心脏骤停患者死亡率的预后模型:可解释的机器学习建模方法
Yong Li1, Ying Liu1, Qing Zhang1
1Department of Anesthesiology, Luzhou People's Hospital, Luzhou, 646000, China.
European journal of medical research
|April 24, 2025
概括
机器学习模型可以预测心脏骤停 (CA) 患者的重症监护室 (ICU) 死亡风险. 随机森林模型证明了早期识别和干预的最佳预测性能.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 由于发病率和死亡率高,心脏骤停 (CA) 构成了重大挑战.
- 早期预测CA的ICU患者的死亡风险对于及时干预至关重要.
研究的目的:
- 开发和验证可解释的机器学习模型,用于早期预测心脏骤停患者的ICU死亡率.
- 确定这一患者群体中死亡率的关键预测因素.
主要方法:
- 利用MIMIC-IV和eICU数据库中的数据进行培训,内部验证和外部验证.
- 开发并比较了五种机器学习模型:物流回归,随机森林,K最近邻居,决策树和极端梯度提升.
- 用于变量选择的雇佣最小绝对缩小和选择运算符 (LASSO) 回归以及用于模型解释性的沙普利增量解释 (SHAP).
主要成果:
- 随机森林 (RF) 模型实现了最高的预测准确性,在内部验证集中ROC曲线下的面积 (AUC) 为0.83,在外部验证集中为0.71.
- 通过SHAP分析确定的ICU死亡率的关键预测因素包括最低格拉斯哥昏迷量表 (GCS),基数过剩,离子间隙和尿液输出.
- 选择了7个变量用于使用LASSO回归的模型构建.
结论:
- 随机森林模型是预测ICU心脏骤停患者的死亡风险的最佳方法.
- 这种可解释的模型有助于早期识别高风险患者,使得有针对性的干预措施能够改善结果.
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