摘要：

糖尿病作为全球4种主要的非传染性疾病之一, 死亡率连年上升。糖尿病患者若长期血糖偏高会引发一系列并发症, 并产生严重的不良后果。预测和控制血糖浓度是糖尿病诊断和治疗中的一个关键问题, 尽管近年来连续血糖监测(CGM)技术的发展已部分解决人工检测带来的不便, 但是CGM设备不仅昂贵还易受外在因素干扰。基于深度学习方法提出一个患者血糖浓度水平预测模型PBI-CLA。首先, 在模型的卷积神经网络(CNN)层通过一维卷积将血糖浓度序列和胰岛素剂量序列的数据特征提取出来; 其次, 在模型的长短期记忆(LSTM)层学习时间序列步长的关联关系; 最后, 模型的注意力层对每个测定血糖浓度时间节点中注射胰岛素剂量赋予不同的权重, 输出得到血糖浓度的预测值。实验结果表明, 该模型的均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)3个指标均有大幅下降, 与其他血糖浓度预测模型相比, PBI-CLA模型1 h血糖浓度预测的RMSE和MAPE分别下降了12.82和10.24百分点。

关键词: 糖尿病, 血糖浓度预测, 深度学习, 卷积神经网络, 长短期记忆, 注意力机制

Abstract:

Diabetes, one of four major global noncommunicable diseases, has seen an increase in mortality rates over the years. Patients with diabetes and chronically high blood glucose levels may experience various complications and serious adverse consequences. The accurate prediction and control of blood glucose levels are critical for the diagnosis and treatment of diabetes. Although Continuous Glucose Monitoring (CGM) technology has alleviated some challenges associated with manual detection, it remains costly and vulnerable to external interference. This study proposes the PBI-CLA model, which is based on deep learning, for predicting blood glucose concentration levels in patients. First, the Convolutional Neural Network (CNN) layer extracts data features from blood glucose concentration and insulin dose sequences through one-dimensional convolution. Subsequently, the Long Short-Term Memory (LSTM) layer learns the correlations between time series increments. Finally, the attention layer assigns different weights to the insulin dosage injected at each time node to measure the blood glucose concentration and outputs the predicted blood glucose concentration value. In extensive experiments, the model reduces the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) substantially. Compared with other glucose concentration prediction models, the RMSE and MAPE values achieved by PBI-CLA for one-hour glucose concentration prediction decrease by 12.82 and 10.24 percentage points, respectively.

Key words: diabetes, blood glucose concentration prediction, deep learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), attention mechanism