  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.
