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机器学习或传统的统计方法用于外科手术期间医学预测建模:叙述性综述

Jason Mann¹, Mathew Lyons², John O'Rourke³

¹Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Anaesthesia and Operating Services, C-floor, Glossop Road, Sheffield, South Yorkshire S11 2JF, UK.

Journal of clinical anesthesia

|February 20, 2025

概括

机器学习 (ML) 在改善外科手术期预测结果模型方面表现有前途,但好处取决于上下文. 高质量的报告和可解释性对于医学中ML的临床整合至关重要.

科学领域:

在外科手术期间的医学.
医疗信息学医疗信息学
机器学习应用机器学习应用

背景情况:

准确预测术后结果对于临床决策和风险沟通至关重要.
与传统的统计模型相比,机器学习 (ML) 越来越多地被探索以提高预测准确性.
有关ML研究的质量和性能提升的临床意义存在担忧.

研究的目的:

审查和评估开发外科手术期间预测性ML模型的研究.
将ML模型的预测性能与传统的统计模型进行比较.
确定影响ML在术后预测中成功应用的因素.

主要方法:

由于研究群体和结果的异质性,进行了叙述性审查.
从37项通过系统搜索,选和全文审查识别的研究中提取了数据.
研究重点是开发和验证外科手术期间预测模型.

主要成果:

几项研究表明,ML可以增强外科手术预测模型.
机器学习提供的性能提升不是普遍的,仍然取决于上下文.
传统的统计模型也在继续发展,并显示出相关的性能.

结论:

机器学习模型显示了增强外科外科预测结果的传统方法的潜力.
临床效用取决于患者为中心的结果,可解释性和外部验证等因素.
报告和方法透明度的高标准对于在临床实践中推进ML至关重要.

关键词:

麻醉学麻醉学机器学习机器学习医学统计数据医学统计数据在外科手术期间的医学.预测建模的预测建模.

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