机器学习支持的临床信息系统使用快速医疗互操作性资源数据标准:范围审查
Jeremy A Balch1,2, Matthew M Ruppert2,3, Tyler J Loftus1,2
1Department of Surgery, University of Florida Health, Gainesville, FL, United States.
JMIR medical informatics
|August 30, 2023
概括
使用快速医疗互操作资源 (FHIR) 的机器学习支持的临床信息系统 (ML-CIS) 是有希望的,但在应用方面有所不同. 建议提出指导方针,以优化未来的ML-CIS开发,以便更好地提供医疗保健和研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床信息系统 临床信息系统
背景情况:
- 支持机器学习的临床信息系统 (ML-CIS) 对于推进医疗保健提供和研究至关重要.
- 快速医疗互操作资源 (FHIR) 标准在ML-CIS开发中越来越多地被采用.
- 目前用于将FHIR应用于ML-CIS的方法表现出变化.
研究的目的:
- 评估和比较现有的基于FHIR的ML-CIS功能,优点和弱点.
- 提出指导方针,以优化未来ML-CIS的发展.
- 确定在临床环境中将机器学习与FHIR标准集成的最佳实践.
主要方法:
- 在Embase,PubMed和Web of Science进行了系统的文献搜索.
- 分析了描述用于临床数据分析或决策支持的FHIR兼容机器学习系统的文章.
- 系统基于功能,数据源,格式,安全性,性能,资源需求和可扩展性进行了比较.
主要成果:
- 关于基于FHIR的ML-CIS的39篇文章被分类为决策支持 (n=18),数据管理 (n=10) 和辅助模块 (n=11).
- 优势包括新的云系统应用程序,贝叶斯网络,可视化和非结构化数据翻译到FHIR.
- 确定的弱点包括缺乏电子健康记录的互操作性和临床疗效的外部验证.
结论:
- 目前的ML-CIS缺陷可以通过模块化,可互操作的数据管理和分析平台来缓解.
- 安全的机构间数据交换和可扩展的API对于实时和潜在的临床应用至关重要.
- 优化ML-CIS需要解决各种电子健康记录平台的实施问题,并确保强大的互操作性.
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