A Depth Evidence Score Fusion Algorithm for Chinese Medical Intelligence Question Answering System

Abstract

Question answering (QA) system is becoming the focus of the research in medical health in terms of providing fleetly accurate answers to users. Numerous traditional QA systems are faced to simple factual questions and do not obtain accurate answers for complex questions. In order to realize the intelligent QA system for disease diagnosis and treatment in medical informationization, in this paper, we propose a depth evidence score fusion algorithm for Chinese Medical Intelligent Question Answering System, which can measure the text information in many algorithmic ways and ensure that the QA system outputs accurately the optimal candidate answer. At the semantic level, a new text semantic evidence score based on Word2vec is proposed, which can calculate the semantic similarity between texts. Experimental results on the medical text corpus show that the depth evidence score fusion algorithm has better performance in the evidence-scoring module of the intelligent QA system.