摘要
Abstract
Objective To construct a neural network model for positivity/negativity discrimination using deep learning after extracting features of clinical findings from internet-based online medical consultation records.Methods Features of doctor-patient conversations and clinical findings were extracted from online consultation records in the internet and converted into numerical feature datasets.The datasets were shuffled and divided into training set,validation set,or test set.A multi-layer perceptron neural network model was built as the baseline model.Datasets with 3 and 5 feature items were used to train and validated the model sequentially,and finally,a neural network model for handling imbalanced data was created by adding class weights.The models were evaluated with indicators such as precision,recall,and area under the receiver operating characteristic curve(AUC),as well as confusion matrices of prediction results.Results The AUCs of the model trained with 3 feature items,the model trained with 5 feature items,and the model using class weights were 0.6148,0.8067,and 0.8087,respectively,with recall and precision both>0.85.Conclusion The model's accuracy,precision,and recall all exceed 85%,indicating good performance in overall prediction,misdiagnosis control,and missed diagnosis control,making it suitable for clinical auxiliary decision-making.关键词
临床发现/阴阳性判别/特征/类权重/神经网络模型Key words
Clinical findings/Positivity/negativity discrimination/Feature/Class weight/Neural network model分类
医药卫生
