老年护理中的机器学习:使用多维评估数据对模型进行范围审查
Angela Mari Mangio1, Caitlin Miller1, Lakshmi Jayan1
1Centre for Health Services Research, Faculty of Health, Medicine & Behavioural Sciences, The University of Queensland, Brisbane, Australia.
International journal of medical informatics
|November 27, 2025
概括
机器学习 (ML) 模型显示,使用老年评估数据预测老年人的健康结果是有前途的. 然而,验证和报告方面的局限性需要解决,以便在现实世界的临床应用中使用.
科学领域:
- 老年医学 老年医学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 老年医疗评估收集有关老年人的身体,认知,心理和社会健康的全面数据.
- 机器学习 (ML) 提供了利用这些多维数据在老年护理中改善临床决策的潜力.
- 缺乏在老年医学评估数据中对ML应用的系统综合.
研究的目的:
- 系统地审查和描述应用到多维老年医学评估数据的ML模型的数据类型,目的和性能.
- 确定常见的ML算法及其在预测老年人健康结果方面的有效性.
主要方法:
- 根据既定的框架 (Arksey和O'Malley,PRISMA-ScR) 进行了范围审查.
- 在六个数据库中搜索了2012-2024年间对老年患者评估数据应用的ML的同行评审研究.
- 使用PROBAST + AI工具评估方法质量.
主要成果:
- 包括40项研究,主要来自高收入国家,重点关注诸如跌倒,功能衰退,虚弱,死亡率和再入院等结果.
- 在几个研究中,XGBoost是表现最好的算法,但性能指标差异很大.
- 常见的局限性包括样本规模小,缺乏外部验证,校准差,数据不平衡等.
结论:
- 使用老年评估数据的ML模型表明,它有可能预测老年人的健康结果.
- 目前,方法和报告的局限性阻碍了这些ML模型的临床翻译.
- 未来的研究必须优先考虑外部验证,可解释性和整合到临床工作流程中,以便在老年护理中有稳健和道德的应用.
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