A Semantic-based Approach for Exploring Consumer Health Questions Using UMLS

NetWellness is a non-profit web service providing high quality health information. It has been in operation since 1995 with over 13 million visits per year by consumers across the world in recent years. Consumer questions in NetWellness have been answered by medical and health professional faculties at three Ohio partner universities: Case Western Reserve University, the Ohio State University, and University of Cincinnati. However, the resident interface in NetWellness is ineffective in searching existing questions that have already been carefully answered by experts in an easy-to-understand manner. In our previous work, we presented a Conjunctive Exploratory Navigation Interface (CENI) reusing NetWellness' 120 pre-defined health topics in assisting question retrieval. This paper presents a novel semantic-based search interface called Semantic Conjunctive Exploratory Navigation Interface (SCENI), using UMLS concepts as topics. 60,000 questions were tagged by UMLS Concept Unique Identifies (CUIs), with each question allowing multiple possible tags. Using a slightly modified 5-point Likert scale for relevance, SCENI reveals improved precision and relevance (precision: 93.47%, relevance: 4.31) in comparison to CENI using NetWellness' pre-defined topics alone (precision: 77.85%, relevance: 3.3) and NetWellness' resident search interface (precision: 50.62%, relevance: 1.97), on a set of sample queries.