【目的】針對患者評論中的不均衡樣本數據,提出一種基于混合采樣與遷移學習的端到端的卷積神經網絡模型。【方法】采用混合采樣與遷移學習的方法解決樣本不均衡問題,并利用Word2Vec與卷積神經網絡相結合的端到端深度學習架構對患者評論文本進行分布式表示、特征提取以及主題分類。【結果】采用混合采樣與遷移學習的主題識別模型相比,以SVM為代表的傳統機器學習模型以及單一卷積神經網絡模型在準確率、召回率以及F1值上有明顯提升。【局限】本研究的不均衡樣本僅針對在線患者評論文本。【結論】本研究提出的基于混合采樣與遷移學習的患者評論識別模型在應對不均衡樣本問題時能夠有效提升患者評論識別效果。

[Objective] This study proposes a new convolutional neural network model, aiming to process the imbalanced data of online patient reviews. [Methods] First, we established the new model with mixed sampling and transfer learning techniques. Then we used end-to-end deep learning architecture based on Word2 Vector and convolutional neural network for the distributed representation, feature extraction and topic classification of online patient reviews. [Results] Compared with traditional machine learning algorithm represented by SVM and single convolutional neural network, the proposed model significantly improved the accuracy, recall and F1 values.[Limitations] The imbalanced data of this study was only from online patient reviews. [Conclusions] The proposed model could effectively improve the recognition results of imbalanced data.