首页|基于神经网络的糖尿病远端对称性多发性神经病变预测模型的构建与验证

基于神经网络的糖尿病远端对称性多发性神经病变预测模型的构建与验证

Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network

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目的 利用神经网络算法,融合中西医特征数据,研究构建糖尿病并发远端对称性多发性神经病变(DSPN)预测模型.方法 从2017-2022年安徽中医药大学第一附属医院糖尿病住院患者中选取4 107例数据完整的病例.收集一般流行病学资料、实验室检查、中医症状及体征共49项指标,采用神经网络建立预测模型,对变量特征权重值排序,分析DSPN潜在危险因素.使用十折交叉进行验证,通过准确度、敏感度、特异度、阳性预测值、阴性预测值、AUC值衡量模型的性能.结果 DSPN组的平均糖尿病病程比非DSPN组长4年左右(P<0.001).与非DSPN患者相比,DSPN患者出现肢体麻木、肢体疼痛、头晕心悸、神疲乏力、口渴喜饮、口干咽干、视物模糊、小便频多、反应迟钝、面色晦暗、舌紫、脉细、脉涩等中医症状及体征的比例明显更高(P<0.001).DSPN神经网络预测模型的AUC为0.945 3,准确度为87.68%、敏感度为73.9%、特异度为92.7%、阳性预测值为78.7%、阴性预测值为90.72%.结论 中西医特征数据融合对DSPN早期诊断具有更大的临床价值,所建立的神经网络模型具有较高的准确率和诊断效率,可为糖尿病人群DSPN的筛查和诊断提供一种便利、实用的工具.
Objective To construct a prediction model of diabetics distal symmetric polyneuropathy(DSPN)based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine.Methods From the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022,4 071 cases with complete data were selected.The early warning model of DSPN was established by using neural network,and 49 indicators including general epidemiological data,laboratory examination,signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN,and the weight values of variable features were sorted.Validation was performed using ten-fold crossover,and the model was measured by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and AUC value.Results The mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group(P<0.001).Compared with non-DSPN patients,DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb,limb pain,dizziness and palpitations,fatigue,thirst with desire to drink,dry mouth and throat,blurred vision,frequent urination,slow reaction,dull complexion,purple tongue,thready pulse and hesitant pulse(P<0.001).In this study,the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data.The AUC of the model was 0.945 3,the accuracy was 87.68%,the sensitivity was 73.9%,the specificity was 92.7%,the positive predictive value was 78.7%,and the negative predictive value was 90.72%.Conclusion The fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis,and the established model has high accuracy and diagnostic efficacy,which can provide practical tools for DSPN screening and diagnosis in diabetic population.

Diabetic neuropathyNeural network algorithmPrediction modelCharacteristics of traditional Chinese medicineIntegration of traditional Chinese and Western medicine

江爱娟、王璐洁、李家劼、林逸轩、赵进东、方朝晖、申国明

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安徽中医药大学中西医结合学院(合肥 230012)

安徽中医药大学第一附属医院(合肥 230031)

糖尿病神经病变 神经网络算法 预测模型 中医特征 中西医结合

国家自然科学基金安徽省重点研发计划安徽省高等学校自然科学研究重点项目

81874457202104j070200062022AH050480

2024

中国循证医学杂志
四川大学

中国循证医学杂志

CSTPCD北大核心
影响因子:1.761
ISSN:1672-2531
年,卷(期):2024.24(3)
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