Query Expansion Framework Leveraging Clinical Diagnosis Information Ontology

The explosive growth of biomedical literature has made it difficult for biomedical scientists to locate precise articles and keep them up to date with the latest knowledge. In biomedical literature retrieval, the heterogeneity of medical terminologies and jargons suffer from query mismatch (QM). The query expansion approaches significantly improve query mismatch by incorporating and re-weighting additional similar terms in the original query. The reliance on medical ontologies to alleviate QM has garnered significant attention in biomedical literature retrieval. However, sole reliance on these ontologies is not sufficient to retrieve relevant results. Considering the foregoing statement, in this article, we design and implement a fusion query expansion framework by integrating the combination of clinical diagnosis information (CDI) and medical ontology (MO); to improve the query mismatch problem. In the proposed system, we have explored the top three MOs (MeSH, UMLS, SNOMEDCT) to select candidate expansion terms. The outcomes of the ontologies are then integrated, with clinical diagnosis information predicted by the unstructured knowledge bases to get the best query combination leading to more focused BLR. The experimental results procured on Text REtrieval Conference (TREC) Clinical Decision Support (CDS) dataset show that this fusion QE framework performed significantly better when CDI and MeSH ontology used jointly to retrieve articles. Furthermore, our results demonstrate the notable ability of the proposed framework to help search engines to improve QM in biomedical literature retrieval. We expect our proposed approach would assist investigators to use this query combination to retrieve relevant articles.