title: Discovering Related Clinical Concepts Using Large Amounts of Clinical Notes.
abstract: The ability to find highly related clinical concepts is essential for many applications such as for hypothesis generation, query expansion for medical literature search, search results filtering, ICD-10 code filtering and many other applications. While manually constructed medical terminologies such as SNOMED CT can surface certain related concepts, these terminologies are inadequate as they depend on expertise of several subject matter experts making the terminology curation process open to geographic and language bias. In addition, these terminologies also provide no quantifiable evidence on how related the concepts are. In this work, we explore an unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes. Our evaluation shows that we are able to use a data driven approach to discovering highly related concepts for various search terms including medications, symptoms and diseases.
authors.affiliation: RxNLP, Salt Lake City, UT, USA.
authors.forename: Kavita
authors.initials: K
authors.lastname: Ganesan
authors.affiliation: Assistant Professor of Radiation Oncology, University of Utah School of Medicine and Huntsman Cancer Institute, Salt Lake City, UT, USA.
authors.forename: Shane
authors.initials: S
authors.lastname: Lloyd
authors.affiliation: Assistant Professor of Radiation Oncology, University of Utah School of Medicine and Huntsman Cancer Institute, Salt Lake City, UT, USA.
authors.forename: Vikren
authors.initials: V
authors.lastname: Sarkar
keywords: clinical concepts; concept graph; data mining; knowledge discovery; related concepts