机器学习模型用于预测急性胰腺炎相关急性损伤的危急病患者的死亡率
Yamin Liu1, Xu Zhu2, Jing Xue3
1Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
Clinical kidney journal
|October 10, 2024
概括
预测急性胰腺炎相关急性损伤 (AP-AKI) 的死亡率至关重要. 在训练组中,XGBoost机器学习模型在预测住院死亡率方面表现出卓越的表现.
科学领域:
- 临界护理医学 临界护理医学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 在医疗保健中的数据科学.
背景情况:
- 急性损伤 (AKI) 在急性胰腺炎 (AP) 患者中显著增加死亡率.
- 对于重症患者来说,AP-AKI中死亡率的早期和准确预测至关重要.
- 传统模型可能无法完全捕捉AP-AKI死亡率预测的复杂性.
研究的目的:
- 开发和验证用于预测AP-AKI患者住院死亡率的机器学习模型.
- 将XGBoost和随机森林模型的性能与后勤回归进行比较.
- 识别AP-AKI中风险分层的有效计算工具.
主要方法:
- 利用了来自MIMIC-IV,eICU-CRD和西安医院临床数据库的数据.
- 用于用于预测器识别的递归特征消除.
- 开发并验证了使用十倍交叉验证的逻辑回归 (LR),随机森林 (RF) 和极端梯度增强 (XGBoost) 模型.
主要成果:
- 在XGBoost模型中,在训练集中显示出优异的区分和校准 (AUC:0.941).
- 在训练组中,XGBoost获得了最低的Brier分数 (0.039).
- 在外部验证中,XGBoost显示了可接受的性能 (AUC:0.724),但没有显著地超过LR和RF.
结论:
- XGBoost模型是预测AP-AKI患者住院死亡率的有希望的工具,在培训数据中表现优于传统方法.
- 需要进一步验证,以确认这些研究结果在不同患者群体中的通用性.
- 机器学习方法有可能改善重症监护机构的风险分层.
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