可解释的人工智能 (XAI) 在电子健康记录研究中的应用:范围审查
Jessica Caterson1, Alexandra Lewin2, Elizabeth Williamson2
1Imperial College London, London, UK.
Digital health
|November 4, 2024
概括
可解释的人工智能 (XAI) 越来越多地与机器学习 (ML) 和深度学习 (DL) 在电子健康记录 (EHR) 中使用. 然而,对XAI方法的批判性评价.
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
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 和深度学习 (DL) 提供了先进的医疗预测功能,在复杂的电子健康记录 (EHR) 数据上表现优于传统模型.
- 在医疗保健中,ML/DL的实际实施往往受到其固有的缺乏解释性的限制.
- 提出了可解释的人工智能 (XAI) 方法来应对这种可解释性挑战,但它们的有效性需要彻底评估.
研究的目的:
- 批判性地评估XAI方法在ML/DL模型中的应用,使用表式EHR数据.
- 在EHR研究中识别使用XAI技术的趋势.
- 评估XAI在该领域的评估和报告标准的现状.
主要方法:
- 根据PRISMA指南进行的范围审查.
- 搜索了包括PUBMED和OVID/MEDLINE (EMBASE) 在内的相关出版物的数据库.
- 包括2017年2月至2023年6月期间发表的76项研究,重点关注表格式EHR数据,ML/DL模型和XAI.
主要成果:
- 观察到使用XAI与EHR数据的出版物数量显著呈指数增长.
- 极端梯度增强和随机森林是最常见的ML/DL模型.
- 沙普利添加式解释 (SHAP) 是主要的XAI方法,在76项研究中的63项中使用.
结论:
- 虽然XAI在EHR研究中的采用正在增长,但其应用是不一致的,缺乏批判性评估.
- 在报告XAI方法及其有效性/稳定性方面存在明显的缺陷.
- 进一步严格评估XAI方法对于它们在医疗保健机构的安全和有效实施至关重要.
关键词:
人工智能的人工智能是人工智能.数字数字数字数字数字数字数字.数字健康数字健康电子健康 电子健康电子邮件 电子邮件 电子邮件 电子邮件健康的健康健康的健康.医疗信息学 医疗信息学机器学习是机器学习.系统性审查是系统的审查.更多相关视频
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