Retrieving and ranking short medical questions with two stages neural matching model
This work addresses the challenge of handling large-scale medical question data for doctors in internet hospitals, but it appears incremental as it builds on existing neural matching techniques.
The authors tackled the problem of automatically matching and ranking short medical questions to assist doctors in online medical services, proposing a two-stage neural matching model for semantic matching of query-level medical questions.
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.