在临床实验室中基于机器学习的错误检测:批判性审查
Yanchun Lin1, Isaiah K Mensah1, Michelle Doering2
1Department of Pathology, Washington University School of Medicine, St. Louis, MO, USA.
Critical reviews in clinical laboratory sciences
|June 11, 2025
概括
机器学习可以帮助检测实验室测试中的错误,改善患者护理. 本综述检查了目前用于实验室错误的机器学习解决方案,并确定了未来发展的领域.
科学领域:
- 临床化学 临床化学
- 医学诊断 医学诊断 医学诊断
- 医疗信息学 医疗信息学
背景情况:
- 实验室检测结果对于医疗决策至关重要.
- 实验室测试中的错误可能会对患者护理和医疗保健业务产生重大影响.
- 现有的质量保证系统已经提高了可靠性,但需要进一步改进.
研究的目的:
- 审查目前用于识别实验室错误的机器学习 (ML) 应用程序.
- 评估ML在区分生理变异和实际实验室错误方面的有效性.
- 确定实验室质量控制中的ML未得到满足的需求和实施障碍.
主要方法:
- 系统审查已发表的关于用于实验室错误检测的机器学习的文献.
- 批判性评估ML算法及其性能指标.
- 分析当前基于ML的实验室质量保证的挑战和局限性.
主要成果:
- 机器学习在分析复杂数据以检测实验室错误方面表现有前途.
- 目前的ML解决方案在复杂性和应用范围方面各不相同.
- 对于广泛采用,包括数据标准化和验证,仍然存在重大障碍.
结论:
- 机器学习提供了一个强大的工具来提高实验室测试的准确性和可靠性.
- 需要进一步的研究和开发来克服实施挑战.
- 通过减少实验室错误,ML有可能显著提高患者安全和医疗保健效率.
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