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基于机器学习的模型的开发和验证,以预测中年和老年人中孤立的挑战后高血糖症:来自多中心研究的分析

Rui Hou^1,2, Jingtao Dou³, Lijuan Wu¹

¹Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Diabetes/metabolism research and reviews

|July 20, 2024

概括

一种新的人工智能模型可以有效地选隔离后挑战性高血糖症 (IPH),一种经常被误诊的糖尿病形式. 该工具提供了一种方便和用户友好的方法,用于在大量人群中早期检测糖尿病.

科学领域:

内分泌学和糖尿病研究研究
医疗保健中的人工智能
公共卫生查公共卫生查

背景情况:

口服葡萄糖耐受性测试虽然有效,但成本昂贵且复杂,阻碍了糖尿病查的广泛使用.
这导致了孤立的挑战后高血糖症 (IPH) 的错误诊断,其特点是正常的禁食葡萄糖,但食后葡萄糖升高.
需要可访问的查工具来识别一般人群中的IPH.

研究的目的:

开发和验证一个预测模型来区分IPH个体与正常人群的差异.
创建一个方便和用户友好的查工具,用于早期发现糖尿病.

主要方法:

利用了来自中国的54,301名参与者REACTION研究的数据.
在37740名参与者身上开发了使用三种机器学习算法的预测模型.
使用来自16561名参与者的外部数据验证了最佳模型.

主要成果:

一个基于人工神经网络的IPH诊断系统 (IPHDS) 使用十个关键特性被开发出来.
外部验证显示IPHDS的AUC为0.823,表现优于现有模型.
通过开发可访问的在线预测工具,IPHDS实现了75.6%的灵敏度和74.6%的特异性.

结论:

开发的IPHDS作为一种实用和用户友好的查工具,用于在大量人群中识别IPH.
这种工具可以在例行健康检查期间帮助早期发现糖尿病.
与当前选方法相比,该模型显示出更高的性能.

关键词:

人工神经网络的人工神经网络诊断评估的诊断评估的评估.机器学习是机器学习.在挑战后的葡萄糖.2 型糖尿病 2 型糖尿病

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