Retrieving and ranking short medical questions with two stages neural matching model
Xiang Li, Xinyu Fu, Zheng Lu, Ruibin Bai, Uwe Aickelin, Peiming Ge, Gong Liu · 2019
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services. Online medical services become increasingly popular and active. According to US data in 2018, 80 percent of internet users have asked health-related questions online. Numerous data is generated in unprecedented speed and scale. Those representative questions and answers in medical fields are valuable raw data sources for medical data mining. Automated machine interpretation on those sheer amount of data gives an opportunity to assist doctors to answer frequently asked medical-related questions from the perspective of information retrieval and machine learning approaches. In this work, we propose a novel two-stage framework for the semantic matching of query-level medical questions, which takes advantages of sentence similarity-based search engine techniques and Siamese inspired recent recurrent neural network. The two-stage hierarchical design optimises the performance of automatic information retrieval of user queries. Compared against the classical TFIDF search technique as a single-stage, our novel soft search technique performs significantly better. Incorporating an advanced deep learning model as the second stage can improve the results further, which we believe is the new state-of-the-art in the current problem setting with the unique medical corpus from one of the largest online healthcare provider in market.