在初级保健机构中使用机器学习来识别慢性疾病病例:对方法,验证和临床相关性的范围审查

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Anh Nq Pham^1,2, Cliff Lindeman³, Michael Cummings²

¹Department of Health Sciences, Simon Fraser University, Canada.

The Journal of international medical research

|February 20, 2026

概括

人工智能和机器学习可以通过使用电子医疗记录来增强慢性疾病的识别. 然而,提高透明度和验证对于临床应用至关重要.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
慢性疾病流行病学慢性疾病流行病学

背景情况:

电子病历 (EMR) 是慢性疾病监测的丰富数据来源.
从EMR中开发准确的慢性疾病病例定义是一项挑战.
人工智能 (AI) 和机器学习 (ML) 提供了潜在的解决方案.

研究的目的:

审查AI和ML在从初级保健EMR创建慢性疾病病例定义时的应用.
评估这些AI/ML方法的方法透明度.
评估AI/ML衍生的病例定义的临床适用性.

主要方法:

根据Arksey和O'Malley以及PRISMA-ScR指南进行范围审查.
在2000年至2024年期间搜索了主要数据库 (MEDLINE,EMBASE,CINAHL,Scopus,Web of Science)
分析了23项研究,重点关注数据源,ML技术,验证和知识翻译.

主要成果:

监督的ML,特别是随机森林和神经网络,主导了这些研究.
常见的验证指标包括灵敏度和特异性;外部验证不常见.
在约50%的研究中得出可解释的定义;没有正式的知识翻译.

结论:

人工智能和机器学习有望改善基于EMR的慢性疾病识别和监测.
需要提高透明度,强大的验证和临床医生-ML合作.
需要进一步开发以创建可靠的初级保健决策支持工具.

关键词:

人工智能的人工智能是人工智能.电子健康记录电子健康记录方法论方法论方法论初级医疗保健是一项主要的医疗保健服务.

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