A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese

摘要：

　　The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1=77% and P@5=84%, which outperformed all other compared methods.

关键词： Natural Language Processing Neural Networks (Computer) Supervised Machine Learning

作者: Guo Yu Tang Yuan Ni Guo Tong Xie Xin Li Fan Yan Ling Shi

作者单位: IBM Research,China,Beijing,China Pfizer,Beijing,China

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 604-608

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）