使用机器学习方法的脆弱性建模:系统性审查与讨论突出的问题
IEEE journal of biomedical and health informatics
|July 18, 2024
概括
机器学习模型可以使用例行收集的数据来预测老年人的脆弱性. 先进的方法显示出个性化的脆弱性评估和改善老龄化人口的医疗保健的希望.
科学领域:
- 老年学是一门学科.
- 计算健康 计算健康
- 生物统计学 生物统计学
背景情况:
- 虚弱显著影响老年人的健康和生活质量.
- 当前脆弱性建模通常使用低于最佳的,简单的分析技术.
- 机器学习应用在脆弱性方面缺乏大规模的系统审查.
研究的目的:
- 探索机器学习方法来预测或分类老年人的脆弱性.
- 系统地审查现有关于机器学习在脆弱模型中的文献.
主要方法:
- 181篇研究文章的系统审查.
- 分析方法的分类为通用线性模型,生存模型和非线性模型.
- 预测变量和预测结果的分析.
主要成果:
- 审查的方法与现有的脆弱性得分和不良结果的预测有效性有中度的一致性.
- 非线性方法显示出超越通用线性方法的潜力.
- 关键预测因素包括诊断,功能表现和认知;结果包括死亡率和住院.
结论:
- 机器学习为改善脆弱性预测和分类提供了潜力.
- 经典方法和横截面数据是常见的,但纵向数据和先进的ML方法正在出现.
- 未来的研究应该专注于先进的机器学习,用于个性化脆弱工具的高维纵向数据.
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