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开发和验证可解释的马尔科夫嵌入式多标签模型,用于预测外科住院患者多次术后并发症的风险:多中心前性队列研究
Xiaochu Yu1, Luwen Zhang2, Qing He3
1Department of Nephrology.
International journal of surgery (London, England)
|October 13, 2023
概括
现有的工具无法预测多次手术后并发症. 一个新的MARKov-EmbeDded (MARKED) 模型可以准确地同时预测多种并发症,改善高风险患者的临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床决策支持 临床决策支持
背景情况:
- 目前的风险评估工具不足以管理多种相互关联的术后并发症.
- 缺乏有效的并发症管理的定量基础.
- 迫切需要能够同时预测多种并发症的可解释模型.
研究的目的:
- 开发和验证可解释的多标签模型,用于同时预测多个术后并发症.
- 将拟议模型的性能与现有方法进行比较.
主要方法:
- 一个MARKov-EmbeDded (MARKED) 多标签模型是使用大型多中心队列 (50,325名住院患者) 开发的.
- 该模型在不同的患者组上进行了训练和验证,并根据来自单独医院的数据进行了外部验证.
- 使用接收器操作特征曲线 (AUC) 下的面积来评估性能,并使用Shapley添加式解释来评估模型的可解释性.
主要成果:
- 标记模型在外部验证集中的八个结果中实现了最高的平均AUC (0.818),超过了二进制相关性,完全连接网络和深度神经网络.
- 标记显示高AUC (>0.9) 预测心脏并发症,神经并发症和死亡率.
- 关键的手术前预测因素包括血清白蛋白,手术专业,紧急状态,ASA得分,年龄和性别. 重要的是,发现并发症之间的相互作用比手术前变量更有影响力.
结论:
- 与现有方法相比,MARKED模型为预测多种手术后并发症提供了卓越的性能和可解释性.
- 这种可解释的多标签方法为识别高风险患者和推断风险来源提供了宝贵的见解,从而支持临床决策.
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