DEPLOYR:用于在电子病历中部署定制实时机器学习模型的技术框架
Conor K Corbin1, Rob Maclay2, Aakash Acharya3
1Department of Biomedical Data Science, Stanford, California, USA.
我们开发了DEPLOYR,这是一种技术框架,用于将机器学习模型部署到电子医疗记录系统中. 这一框架使实时监测和前性评估成为可能,解决了人工智能工具临床翻译方面的差距.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的机器学习
- 医疗信息技术 (IT) 是一种健康技术.
背景情况:
- 医疗机构需要强大的框架来在临床工作流程中实施机器学习 (ML) 模型.
- 现有的治理框架需要技术解决方案,以有效,安全和高质量的模型部署.
- 弥合ML研究和临床应用之间的差距对于改善患者护理至关重要.
研究的目的:
- 引入DEPLOYR,用于实时部署和监控由研究人员创建的ML模型的技术框架.
- 为了使ML模型能够无集成到广泛使用的电子医疗记录 (EMR) 系统中.
- 为在医疗保健机构中部署ML模型提供最佳实践信息.
主要方法:
- DEPLOYR促进了由EMR行动和实时数据收集引发的模型部署,用于推断.
- 它包括在临床医生工作流程中显示推断的机制,并随着时间的推移监测模型性能.
- 该框架支持无声部署和部署模型的前性评估.
主要成果:
- 使用DEPLOYR无声部署和评估12个ML模型,预测EMR系统中的实验室诊断结果.
- 模型是由EMR中的临床医生操作 (按点击) 触发的.
- 前性评估表明,与回顾性绩效估计的差异存在差异.
结论:
- 由于追溯和前性估计之间的性能差异,在医疗保健中无声部署ML模型是可行的和必要的.
- 预期性能测量应该指导模型部署的最终决策.
- DEPLOYR旨在促进将ML模型转化为临床实践.
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