Capturing Health Information from Research Ontologies (CHIRON)

Project Summary Abstract The volume of published evidence in biomedicine is growing at a rapid pace Doctors and researchers must keep pace with the rapid generation of new evidence to provide up to date care The review and aggregation of knowledge is a time intensive process that must be repeated to assimilate the latest evidence Furthermore published evaluations of evidence e g literature reviews meta analyses systematic reviews are not in a format that is able to leverage modern data analytics Charles River Analytics proposes to design and demonstrate a toolkit for Capturing Health Information from Research Ontologies CHIRON CHIRON uses human in the loop approaches to build models of research designs and attributes based on an ontological framework It leverages a Systemic Functional Grammar SFG toolkit for rapidly capturing model data and intuitive user interfaces ensuring accuracy The format of the research models facilitates their sharing and reuse This sharable format enables doctors and researchers to rapidly aggregate models and assimilate new representations of evidence into an existing body of research using semantic queries Furthermore they facilitate qualitative analyses to obtain summaries and insights about the aggregated literature The proposed workflow encompassing capture analysis and visualization of research models will decrease the amount of time required to find and aggregate relevant research Project Narrative Doctors and researchers require novel tools for finding and evaluating medical evidence to keep pace with the rapidly growing volume of research and provide up to date care A principal barrier to the timely review of medical literature is the time it takes to locate relevant studies examine the research design and then aggregate the data across a body of literature To increase the efficiency of the review process the research and medical community would benefit from tools that allow reviewers to capture models of published research that can be aggregated and combined across a research domain perform qualitative analytics on the aggregated research and visualize the results to obtain insights that guide the evaluation of evidence