For the diagnosis, prognosis, and treatment of their patients, clinicians need
access to accurate, succinct, updated, and trustworthy information, provided by
renowned medical organizations and disseminated through patient guidelines,
medical textbooks, journals, and synoptic overviews. Medical specialists must
search and synthesize information on focused, yet esoteric, questions from a
broad set of literature sources (textbooks, guidelines, journal articles) in the
course of a busy practice using search engines (e.g., ClinicalKey { https://
www.clinicalkey.com/). There are several barriers for clinicians to do focused
clinical search queries at the point of care (e.g., \Drug for condition X?", \Cause
of symptom Y?"): growth and evolution of medical knowledge, insu cient time,
unanswered questions, search on patient comorbidities, lack of awareness on
which resource to search, and lack of trust around quality of search results [
Advanced technological solutions that automate the search and retrieval of
the right excerpts from a corpus of trusted medical literature sources in the
right context for such questions are critical for the practice of medicine and
patient care. Knowledge graphs can be e ective tools to address diverse search
and knowledge inference problems in several domains, including healthcare and
biomedicine [
Given a clinical search query (e.g., ketamine for pain dosage, pancreatic pseudocyst management ), the HGFCSS parses and identi es the set of medical concepts and their semantic types from HG, as well as relevant medical phrases (e.g., dosage) or cohorts (e.g., pregnancy), in that query. The parser uses HG concept embedding vectors (i.e., representation of concepts in a geometric space) and the HG hierarchy and labels to identify similar concepts and to correct misspelled words for query expansion. Medical phrases are mapped to structural elements in literature sources and HG relation types. The natural language query is rewritten to a structured query using the identi ed concepts and relation types, which is then executed against multiple HG indexes through a federated querying infrastructure to retrieve the right excerpt in a medical document. Search performance is measured over multiple sets of real-world clinical queries. 3
Structured representation of medical content in HG improves performance in focused clinical search over conventional text-based search methods. Moreover, the use of concept embedding vectors, in conjunction with HG semantics, further improved the search performance signi cantly, since we were able to parse clinical queries more intelligently. Using automated pipelines to regularly update concepts, labels, and relations within HG from several sources and to tag medical excerpts, we can ensure that clinicians are able to retrieve accurate, recent, succinct, and trustworthy medical content. This research signi es the importance of a scalable healthcare knowledge graph ecosystem and the use of machine learning and knowledge representation methods for focused clinical search.