基于机器学习算法,构建和评估针对接受连续置换治疗的急性损伤患者的死亡率预测模型
Yongbin Wang1,2, Xu Sun2,3, Jianhong Lu1,2
1Department of Intensive Care Unit, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
Annals of medicine
|August 19, 2024
概括
这项研究开发了一种拉索调节后勤回归 (Lasso-LR) 模型,用于预测急性损伤 (AKI) 患者的临床死亡率,这些患者正在接受连续置换疗法 (CRRT). 拉索-LR模型表现出最佳性能,并被视觉化为临床使用的名图.
科学领域:
- 关键护理医学 关键护理医学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学与机器学习
背景情况:
- 急性损伤 (AKI) 是重症患者常见的并发症.
- 连续脏替代疗法 (CRRT) 经常用于在重症监护室 (ICU) 管理AKI.
- 在这种患者群体中预测住院死亡率仍然具有挑战性.
研究的目的:
- 构建和评估机器学习 (ML) 模型,用于预测接受CRRT的重症患者的住院死亡率.
- 为了确定临床应用的最佳ML模型.
- 为个性化风险预测开发一个名图.
主要方法:
- 使用MIMIC-IV数据库和湖州中央医院重症监护室数据进行了回顾性研究.
- 开发了9个ML模型,包括拉索调整后勤回归 (Lasso-LR).
- 使用AUROC,校准曲线和决策曲线分析 (DCA) 的模型的外部验证.
主要成果:
- 拉索-LR模型在外部验证队列中获得了最高的AUROC (0.756).
- 该模型显示了最佳的校准和临床实用性,如DCA所示.
- 基于拉索-LR模型开发了一个静态和动态的名图.
结论:
- 拉索-LR模型是一种有效的工具,用于预测CRRT的重症AKI患者的住院死亡率.
- 开发的诺米图为个性化风险评估提供了一个实用的工具.
- 这种预测模型可以帮助临床医生管理接受CRRT的重症AKI患者.
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