范围审查:在医疗保健系统管理中的机器学习干预措施
Oritsetimeyin V Arueyingho1, Anmar Al-Taie1, Claire McCallum2
1School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM), Centre for Doctoral Training in Digital Health and Care, University of Bristol, UK.
Digital health
|October 24, 2024
概括
机器学习通过分析大数据来优化医疗保健管理,以获得更好的决策和系统改进. 这种人工智能应用程序提高了医疗机构的患者护理和运营效率.
科学领域:
- 医疗保健管理的管理
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 有效的医疗保健管理依赖于平衡人力资源,数据系统,服务提供,药品获取,财务和领导力.
- 来自医疗保健的大数据,包括健康记录和基因组数据,对于运营效率和患者参与至关重要.
- 机器学习 (ML) 是人工智能 (AI) 的一个子集,为优化组织和分析这些复杂的医疗数据提供了工具.
研究的目的:
- 审查机器学习在医疗管理中的主要应用.
- 识别用于医疗保健数据分析的常用ML算法.
- 评估ML在医疗保健领域的局限性和基于证据的益处.
主要方法:
- 按照PRISMA指南进行了系统的文献搜索.
- 搜索的数据库包括IEEE Xplore,ScienceDirect,ACM数字图书馆和SCOPUS在2011年至2021年间发表的文章.
- 收录标准要求同行评审的英语文章含有"医疗保健"",管理"和"机器学习"等关键词.
主要成果:
- 从51篇相关文章的初始搜索中,有6篇文章符合纳入标准.
- 识别的ML算法包括主题建模,动态聚类,神经网络,决策树和集体分类器.
- 应用范围包括电子健康记录,聊天机器人和多种疾病预测系统.
结论:
- 机器学习通过增强决策过程,显著支持医疗保健管理.
- 机器学习有助于医疗机构内部有效处理大数据.
- 从ML中获得的见解为医疗保健系统的实质性改进提供了基础.
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