Focused Clinical Query Understanding and Retrieval of Medical Snippets powered through a Healthcare Knowledge Graph
Maulik R. Kamdar, Michael Carroll, Will Dowling, Linda Wogulis, Cailey, Fitzgerald, Matt Corkum, Danielle Walsh, David Conrad, Craig E. Stanley, Jr.,, Steve Ross, Dru Henke, Mevan Samarasinghe

TL;DR
This paper presents a healthcare knowledge graph-powered system that interprets clinical search queries to retrieve relevant medical snippets, aiding clinicians in accessing accurate and trustworthy medical information efficiently.
Contribution
It introduces a novel approach combining a healthcare knowledge graph with query understanding to improve medical information retrieval for clinicians.
Findings
Enhanced retrieval accuracy for clinical queries
Improved synthesis of medical information from diverse sources
Facilitated quick access to trustworthy medical snippets
Abstract
Clinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Health Sciences Research and Education · Semantic Web and Ontologies
