基于机器学习的临床决策支持系统对决策,护理提供和患者结果的影响:范围审查
Anindya Pradipta Susanto1,2, David Lyell1, Bambang Widyantoro2,3
1Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia.
基于机器学习 (ML) 的临床决策支持 (CDS) 系统在医疗保健中越来越多地被评估,主要用于图像识别和风险评估. 虽然结果不尽相同,但需要在不同的环境中进一步评估.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 机器学习 (ML) 越来越多地集成到临床决策支持 (CDS) 系统中.
- 评估基于ML的CDS对医疗保健提供和患者结果的影响至关重要.
研究的目的:
- 总结现有的临床环境中基于ML的CDS系统的研究文献.
- 确定基于ML的CDS的应用和评估的趋势.
主要方法:
- 按照PRISMA-ScR指南进行了系统范围审查.
- 搜索了四个主要的数据库 (PubMed,Medline,Embase,Scopus) 来查找2016年1月至2021年4月的研究.
- 提取了关于研究设计,设置,临床/CDS任务,ML方法,CDS自主性和报告影响的数据.
主要成果:
- 包括32项研究,主要来自于中等/三级护理的发达国家.
- 常见的应用包括图像识别/解释和风险评估,通常在辅助CDS角色中.
- 报告对决策,护理提供和患者结果的影响是混合的.
结论:
- 基于ML的CDS正在各种临床领域进行探索.
- 有机会评估基于ML的CDS影响,特别是在资源有限的环境中.
- 需要进一步的研究来优化基于ML的CDS,以改善医疗保健结果.
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