Retrieving and ranking short medical questions with two stages neural matching model

TL;DR

This paper introduces a two-stage neural matching model designed to retrieve and rank short medical questions effectively, leveraging semantic understanding to improve medical data retrieval.

Contribution

It proposes a novel two-stage neural framework for semantic matching of medical questions, enhancing retrieval accuracy in medical data mining applications.

Findings

Improved retrieval accuracy for medical questions

Effective semantic matching in medical data

Enhanced ranking of relevant medical queries

Abstract

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.