术后风险评分:预测,陷和进展情况
Jonathan P Bedford1,2, Oliver C Redfern1, Benjamin O'Brien3,4
1Kadoorie Centre for Critical Care Research and Education, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford.
Current opinion in anaesthesiology
|November 11, 2024
概括
术后风险评分有助于分层患者的风险,但需要更新可靠性. 机器学习显示了改善这些预测工具的前景,提高了临床效用和患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床决策支持 临床决策支持
背景情况:
- 已建立的外科手术期风险评分往往缺乏概括性,需要经常更新.
- 现有的模型可能无法完全捕捉复杂的患者数据和相互作用.
- 机器学习的进步为更强大的风险分层提供了潜力.
研究的目的:
- 在外科手术期间风险评估中审查关键模型性能指标.
- 突出风险预测模型开发中的常见陷.
- 检查当前的外科手术期间风险评分,其局限性以及未来的方向.
主要方法:
- 关于外科手术期间风险评分和机器学习应用的当前文献的审查.
- 对模型性能指标和开发陷的分析.
- 检查新的风险评分和基于机器学习的方法.
主要成果:
- 在较大的队列中开发的较新的外科手术期间风险评分显示出优异的性能.
- 机器学习技术显示了有效利用多维数据的潜力.
- 先进模型的临床整合需要进一步验证,并专注于可用性.
结论:
- 强大的模型开发和验证对于所有外科手术期间风险得分至关重要.
- 机器学习的进步,包括多样化的数据集成,有望提高预测性能.
- 未来的研究应该优先考虑模型的解释性和持续学习,以提高临床效用.
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