Wang Jason K, Schuler Alejandro, Shah Nigam H, Baiocchi Michael T M, Chen Jonathan H
Mathematical & Computational Science Program, Stanford University, Stanford, CA, USA.
Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:226-235. eCollection 2018.
Clinical order patterns derived from data-mining electronic health records can be a valuable source of decision support content. However, the quality of crowdsourcing such patterns may be suspect depending on the population learned from. For example, it is unclear whether learning inpatient practice patterns from a university teaching service, characterized by physician-trainee teams with an emphasis on medical education, will be of variable quality versus an attending-only medical service that focuses strictly on clinical care. Machine learning clinical order patterns by association rule episode mining from teaching versus attending-only inpatient medical services illustrated some practice variability, but converged towards similar top results in either case. We further validated the automatically generated content by confirming alignment with external reference standards extracted from clinical practice guidelines.
从数据挖掘电子健康记录中得出的临床医嘱模式可以成为决策支持内容的宝贵来源。然而，根据所学习的人群，众包此类模式的质量可能存在疑问。例如，从以医师培训团队为主且强调医学教育的大学教学服务中学习住院患者的实践模式，其质量是否会与仅专注于临床护理的主治医生医疗服务有所不同尚不清楚。通过关联规则事件挖掘，分别从教学型和仅主治医生参与的住院医疗服务中学习临床医嘱模式，结果显示了一些实践差异，但在两种情况下都趋向于类似的顶级结果。我们通过确认与从临床实践指南中提取的外部参考标准一致，进一步验证了自动生成的内容。
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