机器学习方法在预测老年人临床结果中的应用 - - 系统性审查和元分析
Robert T Olender1, Sandipan Roy2, Prasad S Nishtala3
1Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK. rto20@bath.ac.uk.
BMC geriatrics
|September 14, 2023
概括
机器学习模型显示出强大的预测老年人死亡率的能力. 需要进一步验证,以将这些预测工具整合到老年护理中,以获得更好的健康结果.
科学领域:
- 老年医学 老年医学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 对改善老年护理具有重大前景.
- 预测模型可以帮助管理老年人的健康结果.
研究的目的:
- 系统地审查和元分析ML模型在预测老年人 (≥65岁) 临床结果方面的表现.
- 为了比较ML模型在6个月内和之后的死亡率的预测准确度.
主要方法:
- 进行了系统性审查和随机效应元分析.
- 包括八项预测死亡率的研究,按结果时间 (≤6个月和>6个月) 分组.
- 模型性能是使用接收器操作特征曲线 (AUC) 下的面积来评估的.
主要成果:
- 三十七项研究符合系统审查标准;八项研究被纳入了元分析.
- 由于数据异质性,只能将死亡结果汇总在一起.
- 分析显示,对预测死亡率的ML模型的区分能力很好 (AUC为0.80 ≤6个月,0.81>6个月).
结论:
- 机器学习模型在预测老年人死亡率方面表现出强大的歧视力.
- 大规模的验证研究对于临床整合至关重要.
- 未来的努力应该集中在利用不断增长的医疗数据和计算能力来利用ML预测各种临床结果.
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