机器学习模型从电子医疗记录数据中预测孕产妇发病率和死亡率风险:范围审查
Lavanya Vasudevan1,2, Mohammad Golam Kibria3, Lauren M Kucirka4,5
1Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA, 30322, United States, 1 404-727-8812.
Journal of medical Internet research
|August 14, 2025
概括
机器学习模型显示出使用电子医疗记录预测孕产妇健康风险的前景. 然而,目前的研究主要集中在风险预测上,没有研究详细介绍临床应用或实施因素.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 在美国,超过80%的孕产妇死亡是可以预防的,这凸显了主动干预的必要性.
- 使用电子医疗记录 (EMR) 的机器学习 (ML) 模型为预测母亲不良结果提供了一个潜在的策略.
- 现有的审查往往缺乏全面的范围,只关注特定的结果或排除ML模型的完整开发到实施的管道.
研究的目的:
- 系统地审查ML模型的证据,以预测孕产妇发病率和死亡率风险 (RO1).
- 评估这些ML模型的转化为医疗保健提供者的临床应用 (RO2).
- 确定影响ML临床应用在实践中的实施因素 (RO3).
主要方法:
- 在2023年2月20日,在主要数据库 (PubMed,CINAHL Plus,Scopus,Embase,IEEE Xplore) 进行了全面的搜索.
- 研究仅限于在医疗保健环境中使用EMR数据的研究,由独立审稿人严格选标题,摘要和全文.
- 数据提取遵循了一个结构化的模板,结果被描述性地总结.
主要成果:
- 在480项已识别的研究中,包括了39项,其中超过一半是在2022年发表的,主要来自美国,中国和以色列.
- 大多数研究的重点是预测怀孕和分娩的结果,特别是心血管风险,高血压疾病,妊娠糖尿病和产后出血.
- 虽然使用可计算的表型和增强方法的30项研究是常见的,但没有报告关于临床应用 (RO2) 或实施因素 (RO3) 的研究.
结论:
- 未来的研究应该优先考虑产后结果,并通过报告检查清单提高研究透明度和可重复性.
- 在将ML模型转化和实施到孕产妇护理的临床实践中存在重大差距.
- 扩大努力至关重要,以弥合ML模型开发和现实世界的临床应用之间的差距,以防止孕产妇死亡率.
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