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Assessment of Kidney Function in Mouse Models of Glomerular Disease
Published on: June 30, 2018
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糖尿病病的机器学习预测模型:系统审查和元分析
Lianqin Chen1, Xian Shao1, Pei Yu2
1NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
Endocrine
|December 23, 2023
概括
机器学习模型显示出糖尿病病 (DKD) 的良好预测能力. 后勤回归 (LR) 和其他机器学习 (ML) 模型的性能类似,LR是成本效益较高的选择.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 糖尿病病 (DKD) 风险因素的识别正在通过机器学习 (ML) 取得进展.
- 在现实世界DKD研究中对ML模型预测性能的全面系统分析缺乏.
研究的目的:
- 系统地总结ML方法对DKD发病和进展的预测能力.
- 为DKD研究中的ML方法提供一个基本概要.
主要方法:
- 在PubMed,Web of Science,Embase和MEDLINE数据库中搜索了符合条件的研究.
- 分类了各种ML技术,并分析了它们对DKD的预测性能的差异.
主要成果:
- 逻辑回归 (LR) 是最常用的ML方法,其AUROC是0.83.
- 非LR型号也表现出强的表现,AUROC总值为0.80.
- 在LR和非LR模型之间没有发现预测能力的统计学上显著差异.
结论:
- 所有的ML模型都显示了对DKD的令人满意的预测能力 (AUROC > 0.7).
- 非LR型号的表现并没有超过LR型号.
- 由于其高性能,简单性和计算效率,LR是一种实用,具有成本效益的ML模型.
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