Programmatic Theme: Clinical Research Informatics
Abstract: Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.
Learning Objective: - In this research, we will provide an introduction to one of the largest medical knowledge graphs that is curated by domain experts, as well as has semantic relations extracted from medical textbooks and journals using natural language processing pipelines.
- We will showcase how Medical Knowledge Graphs will drive point of care search engines and enable retrieval of precise answers and references given a user query.
- We will discuss some of the other applications that are being developed using H-Graph for advanced clinical decision support and precision medicine.
Maulik Kamdar (Presenter)
Craig Stanley, Elsevier
Michael Caroll, Elsevier
Linda Wogulis, Elsevier
Will Dowling, Elsevier
Helena Deus, Elsevier
Mevan Samarasinghe, Elsevier