方法论的大轮:在质量改进倡议中实施机器学习解决方案的关键考虑因素
Amol A Verma1,2,3,4, Patricia Trbovich2,5,6, Muhammad Mamdani7,2,4
1Unity Health Toronto, Toronto, Ontario, Canada amol.verma@mail.utoronto.ca.
BMJ quality & safety
|December 5, 2023
概括
在医疗保健中实施机器学习 (ML) 解决方案需要仔细考虑. 本指南提供了在临床环境中安全,有效和道德地部署ML的实用策略.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 临床信息学是一种临床信息学.
- 医学中的人工智能.
背景情况:
- 机器学习 (ML) 解决方案是复杂的社会技术系统,越来越多地用于医疗保健.
- 虽然有望改善护理,但实施不善可能会破坏工作流程,加剧不平等,并伤害患者.
- 由于模型的复杂性,数据偏差和对人类影响的不充分理解,ML应用提出了独特的挑战.
研究的目的:
- 总结关于在临床护理中实施ML解决方案的当前知识.
- 为医疗保健中安全,有效和道德地部署ML提供实际指导.
- 为考虑部署ML解决方案的用户提出关键问题.
主要方法:
- 在临床环境中对ML实施的文献综述.
- 综合关于挑战和最佳实践的知识.
- 制定一个框架,为ML部署提出三个指导性问题.
主要成果:
- 机器学习的实施涉及数据,模型,基础设施和人类互动.
- 关键的挑战包括模型的解释性,数据偏差和理解行为影响.
- 三个关键问题涉及问题适用性,评估准备以及最佳部署/维护.
结论:
- 质量改进社区对于安全和道德的ML翻译至关重要.
- 系统的评估和深思熟虑的部署对于成功的ML集成至关重要.
- 解决ML的独特挑战对于实现其在医疗保健中的潜力至关重要.
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