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Mingchuang Zhang¹, Rui Chen¹, Yidi Yang¹

¹Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, China.

Scientific reports

|July 22, 2024

概括

这项研究开发了一种机器学习模型,使用实验室测试来预测减肥手术后30天的再入院情况. 支持矢量机 (SVM) 模型显示了最高的准确性,有效地识别高风险患者.

科学领域:

医疗信息学医疗信息学
手术结果研究研究.
医疗保健中的机器学习

背景情况:

减肥手术是治疗肥胖症的常见手术.
识别高风险再接收的患者对于改善患者护理和降低医疗保健成本至关重要.
预测模型可以帮助主动干预.

研究的目的:

开发和评估机器学习模型,用于预测减肥手术后30天的再入院.
确定与再接收风险相关的关键实验室测试指标.
在这个预测任务中比较各种机器学习算法的性能.

主要方法:

利用了1262名接受减肥手术 (2018-2023) 的患者的数据.
分析了手术前,手术后第一天和手术后第三天的实验室测试指标.
用于特征选择的使用最小绝对收缩和选择运算符 (LASSO) 回归.
构建并比较了支持向量机 (SVM),通用线性模型,多层感知子,随机森林和极端梯度增强模型.
使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.

主要成果:

总共有7.69%的患者在30天内重新入院.
支持矢量机 (SVM) 模型实现了最高的预测性能,AUROC为0.784 (95% CI 0.696-0.872).
SVM模型的表现优于其他评估的机器学习算法.

结论:

使用实验室测试数据的机器学习模型可以有效地预测腹腔外科手术后的30天再入院风险.
SVM模型显示了识别高风险患者的巨大潜力.
这些发现支持将预测分析纳入术后护理途径.

关键词:

腹腔外科手术是什么意思机器学习机器学习预测模型是一个预测模型.再接纳是指重新接纳的人.

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