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机器学习模型用于预测糖尿病患者的血糖水平:系统审查和网络元分析
Kui Liu1, Linyi Li1, Yifei Ma1
1Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China.
JMIR medical informatics
|November 20, 2023
概括
机器学习模型有效地预测糖尿病患者的血糖水平,神经网络模型表现出最佳表现. 虽然对不良事件的预测是足够的,但检测需要改进.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 糖尿病管理 糖尿病管理
背景情况:
- 机器学习 (ML) 模型为糖尿病患者提供了先进的工具来管理血糖 (BG) 水平.
- 选择最合适的ML算法是非常重要的,因为可用的各种各样的.
研究的目的:
- 系统地评估和比较各种ML模型在预测BG水平方面的性能.
- 通过聚合灵敏度和特异性来评估ML模型在检测和预测BG不良事件如低血糖症方面的有效性.
主要方法:
- 在PubMed,Embase,Web of Science和IEEE Xplore进行了系统的文献搜索,直到2022年11月.
- 包括的研究集中在ML模型上,用于在DM患者中预测BG水平或检测/预测不良BG事件.
- 通过使用诊断准确性研究的质量评估工具来评估研究质量.
主要成果:
- 该分析包括了46项研究. 对于BG水平预测的绝对根平均平方误差 (RMSE) 随着预测水平 (PHs) 的增加而增加.
- 神经网络模型 (NNM) 在不同的PH中表现出最高的相对性能.
- ML模型显示了预测低血糖症的8.3和检测低血糖症的2.4的综合正概率比,负概率比分别为0.31和0.37.
结论:
- 在各个子组中观察到很高的异质性,表明影响结果的来源多样化.
- 随着预测地平线的延长,BG级预测准确性下降,NNM是表现最好的人.
- 目前的ML模型足以预测低血糖症,但需要增强才能准确检测.
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