Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities.
Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities.
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DOI：
10.5334/egems.287
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发表时间：
2019-01-24
期刊：
影响因子：
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通讯作者：
Balu, Suresh
中科院分区：
文献类型：
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作者：
Sendak, Mark;Gao, Michael;Balu, Suresh
推动临床护理的完全集成的机器学习模型的例子很少见。尽管在超越临床专家的方法学发展方面取得了重大进展，机器学习在主流医学文献中的地位日益突出，但仍然存在重大挑战。在Duke Health，我们在临床护理中开发、试验和实施机器学习技术已进入第四个年头。为了推动机器学习向临床护理的转化，卫生系统领导人必须解决取得进展的障碍，并进行必要的战略投资，将卫生保健带入新的数字时代。机器学习可以微妙的方式改善临床工作流程，这与统计学如何塑造医学截然不同。然而，大多数机器学习研究都是在竖井中进行的，对于如何在部署后重新培训和验证模型有一些重要的悬而未决的问题。培养和重视跨学科协作的学术医学中心非常适合将机器学习整合到临床护理中。在促进协作环境的同时，卫生系统领导人必须投资于在标准电子健康记录之外的劳动力和技术基础设施中开发新的能力。现在是打破障碍并在临床研究人员和机器学习专家之间的高影响力合作数量上实现可扩展增长的机会，以改变临床护理。
Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. To advance the translation of machine learning into clinical care, health system leaders must address barriers to progress and make strategic investments necessary to bring health care into a new digital age. Machine learning can improve clinical workflows in subtle ways that are distinct from how statistics has shaped medicine. However, most machine learning research occurs in siloes, and there are important, unresolved questions about how to retrain and validate models post-deployment. Academic medical centers that cultivate and value transdisciplinary collaboration are ideally suited to integrate machine learning in clinical care. Along with fostering collaborative environments, health system leaders must invest in developing new capabilities within the workforce and technology infrastructure beyond standard electronic health records. Now is the opportunity to break down barriers and achieve scalable growth in the number of high-impact collaborations between clinical researchers and machine learning experts to transform clinical care.