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机器学习预测模型用于术后的结直肠手术后的结直肠

Luke Traeger^1,2, Sergei Bedrikovetski^1,2, Jessica E Hanna^1,2

¹Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

ANZ journal of surgery

|May 2, 2024

概括

机器学习,特别是MLP模型,在结直肠外科手术患者中有效预测了手术后大肠 (POI). 该研究确定了sarcopenia作为POI的新型,可修改的风险因素,有助于预防策略.

科学领域:

结肠直肠手术结肠直肠手术
机器学习机器学习
预测分析是一种预测分析.

背景情况:

手术后阴道 (POI) 是结直肠手术后的一种严重并发症,影响大约30%的患者.
目前的预防策略在减少POI发病率方面取得了有限的成功.

研究的目的:

应用机器学习技术来识别结直肠手术患者POI的风险因素.
引导制定针对POI的增强预防策略.

主要方法:

一项对504名结直肠手术患者 (2018-2022) 的回顾性研究.
训练并评估多变量逻辑回归,决策树,辐射基函数和多层感知子 (MLP) 模型.
使用接收器操作特征 (AUROC) 曲线下的面积来评估模型性能.

主要成果:

MLP模型实现了最高的AUROC (0.800),优于其他模型.
在183名 (36%) 患者中发生POI.
在MLP模型中,sarcopenia被确定为POI的一个显著的,潜在的可修改的风险因素,除了已知的因素,如低血和阿片类药物使用.

结论:

与传统方法相比,MLP模型显示了POI的优异预测性能.
机器学习为POI的排名预测变量提供了有价值的见解.
术前的肉症需要进一步调查,作为POI的预测因素.

关键词:

结直肠直肠直肠直肠直肠直肠伊利乌斯 (Ileus) 是一个机器学习是机器学习.预测预测预测预测

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