机器学习在预测皮肤穿刺冠状动脉干预后结果的准确性:系统性审查
Caitlin Fern Wee1, Claire Jing-Wen Tan1, Chun En Yau1
1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
AsiaIntervention
|September 30, 2024
概括
机器学习 (ML) 模型在皮肤冠状动脉干预 (PCI) 后预测结果方面表现有希望. 然而,模型开发和验证中的问题需要在临床整合之前解决.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 算法显示了皮肤穿透冠状动脉干预 (PCI) 后预测结果的潜力.
- 现有的PCI后结果的风险分层分数可以通过ML工具来增强.
研究的目的:
- 批判性地评估当前ML模型作为预测PCI后结果的临床工具的有效性.
- 评估心脏病学ML模型的转化途径,从开发到验证和影响.
主要方法:
- 到2021年5月,对四个数据库进行系统的文献搜索,寻找使用ML来预测PCI后结果的研究.
- 提取了诊断准确度指标和批判性评估的研究,使用适应的检查清单,涵盖开发,验证和影响.
- 评估了培训数据的质量和处理缺少数据的方法.
主要成果:
- 包括12个队列 (11项研究,4,943,425名患者),显示具有高诊断准确性的ML模型.
- 在ML模型开发中发现的担忧,包括缺少数据的问题处理和有限的外部验证 (3/11研究).
- 缺乏对实施的ML模型的成本效益分析.
结论:
- 机器学习模型显示出作为预测PCI后结果的临床辅助的前景.
- 必须解决开发,验证和数据处理方面的重大挑战,以便在心脏病学中临床整合ML.
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