评估用于医疗保健领域的自动化机器学习平台。
Evaluating automated machine learning platforms for use in healthcare.
作者信息
Scott Ian A, De Guzman Keshia R, Falconer Nazanin, Canaris Stephen, Bonilla Oscar, McPhail Steven M, Marxen Sven, Van Garderen Aaron, Abdel-Hafez Ahmad, Barras Michael
机构信息
Centre for Health Services Research, University of Queensland, Brisbane, 4102, Australia.
Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, 4102, Australia.
出版信息
JAMIA Open. 2024 Jun 10;7(2):ooae031. doi: 10.1093/jamiaopen/ooae031. eCollection 2024 Jul.
OBJECTIVE
To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.
MATERIALS AND METHODS
Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes.
RESULTS
The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case.
DISCUSSION
A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example.
CONCLUSION
An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.
目的
描述用于选择自动机器学习（Auto ML）平台以创建临床机器学习模型的标准清单的制定与应用。
材料与方法
分三步制定适合当地卫生区机器学习需求的Auto ML平台选择评估标准：（1）确定关键要求；（2）市场调研；（3）具有预期结果的评估过程。
结果
包含21项功能标准和6项非功能标准的最终清单应用于供应商提交的方案，以选择一个平台来创建一个作为用例的机器学习肝素给药模型。
讨论
由临床医生、数据科学家和关键利益相关者组成的团队制定了一份可根据医疗保健组织的机器学习需求进行调整的清单，该用例提供了一个相关示例。
结论
制定了一份用于选择Auto ML平台的评估清单，该清单需要在更大规模的多中心研究中进行验证。
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