【摘要】 现有H型高血压诊断需要检测患者体内的伴有血浆同型半胱氨酸含量，效率低且带有创口。中医学脉诊可以通过分析患者脉搏生理活动，结合临床问诊信息实现H型高血压无创辅助诊断。本文提出了基于混合深度学习的脉诊分类模型，在具有双向长短时记忆（Bi-directional long short-term memory,BiLSTM）网络中增加卷积神经网络（Convolutional neural network,CNN）结构提取脉诊特征局部相关特征，构建基于CNN-BiLSTM结构的高血压脉诊分类网络。实验采用上海中医药大学附属龙华医院及中西医结合医院的325例临床疑似高血压脉诊病例。实验结果表明本文模型评估参数灵敏度、特异性、正确率、F₁-score、接收者操作特征（Receiver operating characteristic,ROC）曲线及其下方围成的面积（Area under curve,AUC）值分别为：79.71%、69.56%、77.17%、83.96%、0.850 0，高于经典机器学习方法的诊断精度，对中医临床辅助诊断具有较好的参考价值。更多还原

【Abstract】 The diagnosis of H-type hypertension requires the determination of the patient’s plasma homocysteine content,which is inefficient and has a wound. Chinese pulse diagnosis helps doctors diagnose H-type hypertension by analyzing patient’s pulse activity and combining inquiry information. Therefore,we put forward a pulse-taking diagnosis classifiction model based on hybrid deep learning model,which can extract the local features via convolutional neural network（CNN） block,and long-term dependency features via Bi-directional long short-term memory（BiLSTM）block. The data come from 325 suspected cases of pulse diagnosis collected by Longhua Hospital affiliated to Shanghai University of Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We compare the proposed model with other machine learning models on the pulse diagnosis data respectively. The sensitivity,specificity,accuracy,F₁-score,receiver operating characteristic（ROC）area under curve（AUC）values of the proposed model are 79.71%,69.56%,77.17%,83.96%,0.850 0,respectively,higher than the performance of other machine learning models. The results show that our model has good performance and has good reference value for the clinical diagnosis of traditional Chinese medicine.更多还原