机器学习算法的临床性能,以预测手术内低血压:一个元分析
Mahdi Faraji1, Narges Norouzkhani2, Anahid Bagheri Pour3
1Student research committee, school of medicine, Ardabil University of medical sciences, Ardabil, Iran.
BMC surgery
|December 18, 2025
概括
机器学习算法对预测手术内低血压,一种常见的手术并发症,显示出有前途. 这一元分析证实了它们的可接受性能,表明了减少手术不良事件的潜在益处.
科学领域:
- 麻醉学和重症监护医学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 术后低血压是手术期间经常出现的不良事件,与术后并发症增加有关.
- 低血压的预测工具可以改善患者的治疗结果和手术安全性.
- 机器学习 (ML) 为实时预测和检测手术内低血压提供了先进的功能.
研究的目的:
- 评估ML算法的临床性能和诊断准确性,以预测手术内低血压.
- 综合从观察性研究中得出的关于ML在低血压预测中的有效性证据.
- 确定随机对照试验 (RCT) 调查人工智能引导的手术内管理.
主要方法:
- 在主要数据库 (PubMed,Scopus,Web of Science,Google Scholar) 进行了系统的文献搜索,直到2024年8月.
- 两位独立审查员选了研究,重点关注观察性准确性研究和RCT.
- 从包括的观察性研究中,对诊断/预后准确度指标 (灵敏度,特异性,AUROC) 的定量综合.
主要成果:
- 包括5项观察性研究,涉及42,509名参与者.
- ML算法利用心电图和血压数据进行预测.
- 聚合分析显示了可接受的性能:特异性为0.73,灵敏度为0.75,AUROC为0.83.
结论:
- ML算法具有可接受的准确性,在预测手术内低血压方面具有优势.
- 在手术环境中集成ML工具有望缓解并发症.
- 需要进一步的研究,包括RCT,以充分确定AI在手术内管理中的临床实用性.
相关概念视频
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Several factors...
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