Paragraph Vector Based Retrieval Model for Similar Cases Recommendation
Internet inquiry is playing an increasingly important role as the complement of the traditional medical service system,especially the similar cases recommendation.It can not only save the patients” waiting time,but also make use of the historical resources,for many cases with the same purpose have been solved perfectly.However,because of the diversity and non-standard of the patients” descriptions,the inquiry platform cannot find the cases with similar semantic easily.Most traditional retrieval methods require the overlap of two sentences,and this is not suitable with the diversity and non-standard descriptions.In this paper,we try to utilize the sentences” semantic representation in a continuous space to understand the cases,and then recommend the similar cases.We also incorporate it into query likelihood language models,trying to get better results.Our experimental data are all collected from a real internet inquiry platform,and the results show that our methods significantly outperform the state-of-the-art translation based methods for similar cases recommendation.
internet inquiry similar cases recommendation distributed representation data mining
Yifei Zhao Jing Wang Fei-Yue Wang Xiaobo Shi
The State Key Laboratory of Management and Control for Complex Systems Institute of Automation,Chine Institute of Smart Healthcare System Qingdao Academy of Intelligent Industries Qingdao,China
国内会议
福州
英文
1-6
2015-12-11（万方平台首次上网日期，不代表论文的发表时间）