title: Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews.
abstract: Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies.
  
  
  
  We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure.
  
  
  
  We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%.
  
  
  
  We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA; Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA. Electronic address: ji.62@osu.edu.
authors.forename: Xiaonan
authors.initials: X
authors.lastname: Ji
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Ritter
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
authors.forename: Po-Yin
authors.initials: PY
authors.lastname: Yen
mesh_terms: D064229:Biological Ontologies; D006801:Humans; D046650:Medical Subject Headings; D012196:Review Literature as Topic; D012660:Semantics; D039061:Systematized Nomenclature of Medicine; D017432:Unified Medical Language System
keywords: Automatic article classification; Biomedical ontologies; Evidence-based practice; Semantic similarity; Systematic review; Text mining