2 research outputs found
Making Study Populations Visible through Knowledge Graphs
Treatment recommendations within Clinical Practice Guidelines (CPGs) are
largely based on findings from clinical trials and case studies, referred to
here as research studies, that are often based on highly selective clinical
populations, referred to here as study cohorts. When medical practitioners
apply CPG recommendations, they need to understand how well their patient
population matches the characteristics of those in the study cohort, and thus
are confronted with the challenges of locating the study cohort information and
making an analytic comparison. To address these challenges, we develop an
ontology-enabled prototype system, which exposes the population descriptions in
research studies in a declarative manner, with the ultimate goal of allowing
medical practitioners to better understand the applicability and
generalizability of treatment recommendations. We build a Study Cohort Ontology
(SCO) to encode the vocabulary of study population descriptions, that are often
reported in the first table in the published work, thus they are often referred
to as Table 1. We leverage the well-used Semanticscience Integrated Ontology
(SIO) for defining property associations between classes. Further, we model the
key components of Table 1s, i.e., collections of study subjects, subject
characteristics, and statistical measures in RDF knowledge graphs. We design
scenarios for medical practitioners to perform population analysis, and
generate cohort similarity visualizations to determine the applicability of a
study population to the clinical population of interest. Our semantic approach
to make study populations visible, by standardized representations of Table 1s,
allows users to quickly derive clinically relevant inferences about study
populations.Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources
Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/