Making Study Populations Visible Through Knowledge Graphs

Abstract

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.Resource Website: https://tetherless-world.github.io/study-cohort-ontology/

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

“…The third most popular group of technologies are ontologies. To support recommendation processes, among the ontologies created are Semantic Sensor Network Ontology [17], Application Feedback Ontology [70], and Study Cohort Ontology (SCO) [66,75]. Some papers reuse ontological models available on the web, such as Semantic Science Integrated Ontology (SIO) [66], DBPedia ontology [77], and SKOS [79].…”

“…Other technologies that stand out in the other groups are SPARQL to query data from the KG [15,79], RDF to populate the KG with data [15,66,67], the Neo4J base to store graph data [70,81], among others.…”

“…In the health sector, we found four proposals related to knowledge graphs. In [66,75], the Study Cohort Ontology (SCO) is presented, which facilitates clinicians to perform population analysis and to generate cohort similarity visualizations. In this way, clinicians can select the most appropriate study populations for trial applicability.…”

Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

“…Chari et al utilized semantic technologies and knowledge graphs to implement a tool that allows users to quickly derive clinically relevant inferences about study populations [27]. They developed a prototype workflow that utilizes an ontology to expose population descriptions in research studies through visual aids.…”

An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model

“…Health surveillance involves the "ongoing systematic collection, analysis, and interpretation of data essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those who need to know" [9]. Researchers have aimed to develop chronic health surveillance systems to assist in chronic health decision-making [10][11][12][13][14][15][16]. The World Health Organization (WHO) developed a conceptual framework for an Urban Public Health Observatory (UPHO) comprised of 3 domains: mission, governance, and knowledge and intelligence, the latter of which incorporates a data management component [17].…”