[1]刘晶波,田海广,金玖.基于深度学习和中医辨证的老年糖尿病并发症预测模型研究[J].西部中医药,2026,39(09):176-180.[doi:10.12174/j.issn.2096-9600.2026.09.34]
　LIU Jingbo,TIAN Haiguang,JIN Jiu.Research on a Prediction Model for Elderly Diabetic Complications Based on Deep Learning and Traditional Chinese Medicine Syndrome Differentiation[J].Western Journal of Traditional Chinese Medicine,2026,39(09):176-180.[doi:10.12174/j.issn.2096-9600.2026.09.34]
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基于深度学习和中医辨证的老年糖尿病并发症预测模型研究()

《西部中医药》[ISSN:2096-9600/CN:62-1204/R]

卷:
39
期数:
2026年09期
页码:
176-180
栏目:
诊断剖析
出版日期:
2026-09-07

文章信息/Info

Title:
Research on a Prediction Model for Elderly Diabetic Complications Based on Deep Learning and Traditional Chinese Medicine Syndrome Differentiation
作者:
刘晶波, 田海广, 金玖
呼和浩特市中医蒙医医院，内蒙古 呼和浩特 010030
Author(s):
LIU Jingbo, TIAN Haiguang, JIN Jiu
Hohhot Traditional Chinese Medicine and Mongolian Medicine Hospital, Hohhot 010030, China
关键词:
糖尿病; 老年人; 并发症; 深度学习; 中医辨证; 预测
Keywords:
diabetes mellitus; the elderly; complications; deep learning; TCM syndrome differentiation; prediction
分类号:
R587.1
DOI:
10.12174/j.issn.2096-9600.2026.09.34
文献标志码:
A
摘要:
目的引入Attention机制和AdaBoost算法，提出一种融合深度学习和中医辨证理论的模型，以提升老年糖尿病并发症的预测准确性。 方法收集2016年至2024年某三甲医院967名老年糖尿病患者数据，在模型构建中整合Attention-AdaBoost算法与中医辨证数据，捕捉患者特征与并发症间的复杂关联；同时对比不同模型性能；评价指标包括准确率、精确率、召回率、F1值及AUC值。 结果在糖尿病肾病预测中，Attention-AdaBoost模型准确率为94.80%、精确率95.30%、召回率94.20%、F1值94.75%、AUC值0.98；在糖尿病视网膜病变预测中，模型准确率为96.70%、精确率97.20%、召回率96.20%、F1值96.70%、AUC值0.99；在糖尿病足预测中，模型准确率为93.80%、精确率94.20%、召回率93.40%、F1值93.79%、AUC值0.97。以上Attention-AdaBoost模型预测结果均优于传统模型和其他Attention结合模型。 结论Attention-AdaBoost模型通过有效融合中医辨证理论与深度学习技术，显著提升了老年糖尿病患者并发症的预测能力，可为早期干预与个体化治疗提供可靠支持，也为中西医结合在糖尿病并发症防治中的应用提供了新思路与新方法。
Abstract:
ObjectiveTo improve the prediction accuracy of diabetic complications in the elderly, this study proposes a model that integrates deep learning with TCM syndrome differentiation theory, incorporating the Attention mechanism and the AdaBoost algorithm. MethodsData were collected from 967 elderly patients with diabetes at a tertiary hospital between 2016 and 2024. In model construction, the Attention-AdaBoost algorithm was integrated with TCM syndrome differentiation data to capture the complex associations between patient characteristics and complications. The performance of different models was compared, and the evaluation metrics included accuracy, precision, recall, F1-score, and AUC value. ResultsIn the prediction of diabetic kidney disease, the Attention-AdaBoost model achieved an accuracy of 94.80%, precision of 95.30%, recall of 94.20%, F1-score of 94.75%, and an AUC of 0.98. For diabetic retinopathy prediction, the model attained an accuracy of 96.70%, precision of 97.20%, recall of 96.20%, F1-score of 96.70%, and an AUC of 0.99. In diabetic foot prediction, the model achieved an accuracy of 93.80%, precision of 94.20%, recall of 93.40%, F1-score of 93.79%, and an AUC of 0.97. The predictive performance of the Attention-AdaBoost model outperformed that of traditional models and other Attention-based combined models across all three complications. ConclusionBy effectively integrating TCM syndrome differentiation theory with deep learning techniques, the Attention-AdaBoost model significantly enhances the predictive capability for complications in elderly diabetic patients. It can provide reliable support for early intervention and individualized treatment, and offers new perspectives and methodologies for the application of integrated traditional Chinese and Western medicine in the prevention and management of diabetic complications.

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备注/Memo

备注/Memo:
内蒙古自治区科技计划项目（2022LYJSZX03）。刘晶波（1982—），女，硕士学位，副主任医师。研究方向：内分泌代谢病的中医药防治。Email：Lj99192@163.com。
更新日期/Last Update: 2026-09-15