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基于深度学习的2型糖尿病病风险预测模型的构建

Chuan Yun¹, Fangli Tang², Zhenxiu Gao³

¹Department of Endocrinology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.

Diabetes & metabolism journal

|April 30, 2024

概括

这项研究使用长短期记忆 (LSTM) 神经网络开发了一种糖尿病病 (DKD) 预测模型. 该模型准确地预测了DKD风险,通过结合糖化血红蛋白 (HbA1c),缩血压 (SBP) 和脉冲压 (PP) 的变化,性能显著提高.

科学领域:

生物医学信息学生物医学信息学
医疗保健中的机器学习
腎病學研究腎病學研究

背景情况:

糖尿病病 (DKD) 构成了严重的健康负担.
准确预测DKD对于及时干预至关重要.
现有的预测模型可能无法完全捕捉疾病动态.

研究的目的:

开发和评估使用长短期记忆 (LSTM) 神经网络的DKD预测模型.
评估关键生理变异对DKD预测准确性的影响.
建立一个可靠的模型,用于早期识别患有DKD风险的患者.

主要方法:

文献审查和医生重点小组确定了DKD风险因素.
使用Pytorch开发了一个长短期记忆 (LSTM) 神经网络.
分析了超过7年的6,040名2型糖尿病患者的数据.
模型性能使用准确度,精度,回忆和接收器操作特征 (ROC) 曲线的曲线下面面积 (AUC) 进行了评估.
研究了糖基化血红蛋白 (HbA1c),缩血压 (SBP) 和脉冲压 (PP) 变化的影响.

主要成果:

基于LSTM的DKD预测模型实现了83%的准确性和0.83.8的AUC.
删除HbA1c,SBP或PP变异性显著降低了模型准确性 (分别为78%,79%和81%,P<0.001).
排除这些变化也大大降低了AUC值 (分别为0.72,0.75和0.77,P<0.05).

结论:

开发的LSTM神经网络模型显示了高准确性和AUC用于DKD风险预测.
将HbA1c,SBP和PP的变化作为特征,可以显著提高模型的性能.
这种方法为改善DKD的早期检测和管理提供了一个有希望的工具.

关键词:

深度学习是一种深度学习.糖尿病,第二类糖尿病.糖尿病性脏病是一种糖尿病性脏病.风险评估风险评估风险评估风险因素风险因素

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