机器学习的应用,以预测医院内死亡率,经过过导管中枢修复后的中枢
Emma O Cruz1, Sara Sakowitz2, Saad Mallick3
1Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA; Department of Computer Science, Stanford University, Palo Alto, CA.
Surgery
|August 9, 2024
概括
机器学习模型准确地预测了透导管中枢修复后的住院死亡率,超过了传统方法. 医疗保险和心力衰竭等关键因素影响死亡风险,指导更好的患者评估.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 过导管 mitra 门修复 (TMVR) 是高风险患者的一种最小侵入性选择.
- 准确预测住院死亡率对于患者的选择和管理至关重要.
研究的目的:
- 开发和验证动态机器学习 (ML) 风险预测模型,用于TMVR后医院死亡率.
- 将ML模型的性能与传统的物流回归进行比较.
主要方法:
- 使用全国再接收数据库 (2016-2020年) 针对成人TMVR住院治疗.
- 雇佣阶级失衡技术 (低采样/SMOTE) 和交叉验证.
- 训练并将ML模型 (Naïve Bayes,随机森林) 与后勤回归进行比较;使用Shapley图表来确定特征的重要性.
主要成果:
- 在2,450名TMVR患者中,住院死亡率为1.8%.
- 原始贝叶斯 (AUC 0.83) 和随机森林 (AUC 0.82) 模型的表现明显优于后勤回归 (P < .001).
- 死亡率的预测因素包括医疗保险,肝病,心力衰竭,功能衰竭和之前的冠状动脉旁路移植;选择性手术和私人保险是保护性的.
结论:
- 与传统方法相比,ML模型可以更好地预测TMVR后的住院死亡率.
- 确定了影响TMVR结果的重要患者因素和并发症.
- 需要进一步的临床验证来完善基于ML的TMVR风险评估.
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