高风险妊娠中可解释的临床决策支持系统:对模型,方法和实施的范围审查
Imad El Badisy1, Bouchra Assarag2, Zakaria Belrhiti3
1AI and Data Science Service, Mohammed VI Center for Research and Innovation, Rabat, Morocco. ielbadisy@cm6.ma.
BMC pregnancy and childbirth
|January 6, 2026
概括
可解释性临床决策支持系统 (CDSS) 提高了母亲高风险怀孕的预防. 本综述强调了将这些透明的人工智能工具整合到临床实践中的方法和挑战.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 孕产妇健康技术 产妇健康技术
背景情况:
- 使用机器学习 (ML) 的临床决策支持系统 (CDSS) 在孕产妇医疗保健方面表现有前途,但面临采用障碍.
- 缺乏透明度,可重现性和工作流集成,阻碍了ML驱动的CDSS在现实世界中使用.
- 可解释的ML方法对于提高CDSS在产科中的可靠性和可用性至关重要.
研究的目的:
- 将可解释的CDSS绘制为母亲高风险怀孕预防和产后管理.
- 审查ML和基于规则的系统,检查它们的特征,实施,验证和可解释性.
- 识别缺口,并为未来的研发提供建议.
主要方法:
- 从PubMed和谷歌学者的文学范围审查.
- 纳入标准侧重于报告可解释输出和临床性能的研究.
- 数据提取包括研究设计,CDSS类型,验证,可解释性技术和结果.
主要成果:
- 包括19项研究,包括ML (随机森林,支持矢量机) 和非ML (基于规则的评分系统) CDSS.
- 通常使用的是SHAP和LIME等后期解释性方法.
- 代码/数据可用性的变量报告和主要的追溯验证限制了可复制性和通用性.
结论:
- 可解释的CDSS对于推进孕产妇医疗保健至关重要,特别是在高风险怀孕管理方面.
- 提高透明度,前性验证和开放科学实践对于临床整合至关重要.
- 将可解释的输出与临床推理对齐将有助于成功采用这些技术.
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