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在腰部手术中术前预测术后尿:机器学习模型的前性验证

Ken Porche¹, Carolina B Maciel², Brandon Lucke-Wold²

¹Department of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA. ken.porche@neurosurgery.ufl.edu.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

|September 28, 2023

概括

这项研究验证了一种机器学习模型,用于预测术后尿 (POUR). 经过验证的模型显示出良好的相关性,支持其在临床环境中用于预测这种常见并发症.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
麻醉学麻醉学
医疗信息学医疗信息学

背景情况:

预测术后尿 (POUR) 是一个临床挑战.
机器学习模型提供了提高预测准确性的潜力.
一个先前开发的模型需要前性验证.

研究的目的:

为了前性验证用于预测术后尿的机器学习模型.
用现实世界的临床数据来评估模型的性能.
确认机器学习在预测术后并发症方面的实用性.

主要方法:

从手术前诊所招募了171名患者.
预测是由四个人在手术前做出的,并与实际的POUR结果进行了比较.
评估者之间的可靠性是使用相互关联系数来评估的.

主要成果:

该车型的整体性能达到0.663 (0.567-0.759).
有了特定的切断值,该模型显示了90.6%的特异性和73.1%的准确性.
观察到高的相关系数 (回归系数为0.889,神经网络系数为0.874).

结论:

预期验证证实了预测模型的性能.
这项研究支持使用机器学习来预测术后尿.
这种方法可以帮助管理和预防术后并发症.

关键词:

腰椎手术是腰椎脊柱的手术.机器学习是机器学习.预测工具是一个预测工具.保持尿液的时间

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