在重症监护病房入院时早期预测死亡率
Yu-Chang Yeh1, Yu-Ting Kuo2, Kuang-Cheng Kuo2
1Department of Anesthesiology, National Taiwan University Hospital, No 7, Chung Shan South Road, Taipei, Taiwan. tonyyeh@ntuh.gov.tw.
BMC medical informatics and decision making
|December 19, 2024
概括
机器学习模型预测了重症监护室 (ICU) 在入院时和24小时后的死亡率. 梯度增强树 (GBT) 显示了早期ICU死亡率检测的最高预测性能.
科学领域:
- 关键护理医学 关键护理医学
- 机器学习在医疗保健中的应用
- 预测分析是一种预测分析.
背景情况:
- 开发准确的模型来预测重症监护室 (ICU) 死亡率对于重症成人患者至关重要.
- 早期预测可以及时进行干预和资源分配.
研究的目的:
- 开发和验证用于预测ICU死亡率的机器学习模型.
- 在ICU入院和入院后24小时评估模型性能.
主要方法:
- 利用两个教学医院的79,657例ICU入院的综合数据集.
- 经过训练和验证的后勤回归,梯度增强树 (GBT) 和深度学习模型.
- 使用接收器操作特征曲线 (AUC) 下面的面积和精度回忆曲线 (PRC) 评估模型性能.
主要成果:
- 梯度增强树木 (GBT) 模型显示了入院时ICU死亡率的最高预测性能 (AUC:0.856).
- 对于预测住院后24小时死亡率的模型,GBT模型也实现了最高的性能.
- 在入院测试数据集中,ICU死亡率为7%,在24小时测试数据集中为17.4%.
结论:
- 机器学习模型,特别是GBT,可以在ICU入院时就提供有关ICU死亡风险的关键信息.
- 24小时预测模型可以提高长时间住入ICU的患者的死亡率预测.
- 这些模型为重症监护机构的早期风险分层提供了有价值的工具.
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