Making Study Populations Visible Through Knowledge Graphs (original) (raw)
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Ontology-enabled Analysis of Study Populations
2019
We address the problem of modeling study populations in research studies in a declarative manner. Research studies often have a great degree of variability in the reporting of population descriptions. To make study populations easily accessible for decision making related to study applicability, we will show the usage of our ontology-enabled prototype system in different applications. Our system leverages our Study Cohort Ontology and the related cohort Knowledge Graph (as described in our accepted resource track paper). We aim to address three retrospective population analysis scenarios, designed to specifically determine the study match, study limitations, and evaluate the study quality. We also provide visualizations of a patient (or patient population) to a treatment arm. In addition, for each guideline recommendation that depends upon a study, we provide a summary of the relevant study’s cohort description. We describe some of our applications and their potential impacts. Resou...
SemanticDB: A Semantic Web Infrastructure for Clinical Research and Quality Reporting
Semantic Web technologies offer the potential to revolutionize management of health care data by increasing interoperability and reusability while reducing the need for redundant data collection and storage. From 1998 through 2010, Cleveland Clinic sponsored a project designed to explore and develop this potential. The product of this effort, SemanticDB, is a suite of software tools and knowledge resources built to facilitate the collection, storage and use of the diverse data needed to conduct clinical research and health care quality reporting. SemanticDB consists of three main components: 1) a content repository driven by a meta-model that facilitates collection and integration of data in an XML format and automatically converts the data to RDF; 2) an inference-mediated, natural language query interface designed to identify patients who meet complex inclusion and exclusion criteria; and 3) a data production pipeline that uses inference to generate customized views of the repository content for statistical analysis and reporting. Since 2008, this system has been used by the Cleveland Clinic's Heart and Vascular Institute to support numerous clinical investigations, and in 2009 Cleveland Clinic was certified to submit data produced in this manner to national quality monitoring databases sponsored by the Society of Thoracic Surgeons and the American College of Cardiology.
2012
Clinical trials for drug repositioning aim at evaluating the effectiveness and safety of existing drugs as new treatments. This involves managing and semantically correlating many interdependent parameters and details in order to clearly identify the research question of the clinical trial. This work, which is carried out within the PONTE (Efficient Patient Recruitment for Innovative Clinical Trials of Existing Drugs) project, aims to improve the trial design process, by not only offering access to a variety of relevant data sources – including, but not limited to, drug profiles, diseases and their mechanisms, genes and past trial results – but also providing the ability to navigate through these sources, perform queries on them and intelligently fuse the available information through semantic reasoning. This article describes our intention to consume and aggregate information from Linked Data sources in order to produce answers for the clinical researcher’s
Interpreting Medical Tables as Linked Data for Generating Meta-Analysis Reports
Evidence-based medicine is the application of current medical evidence to patient care and typically uses quantitative data from research studies. It is increasingly driven by data on the efficacy of drug dosages and the correlations between various medical factors that are assembled and integrated through meta--analyses (i.e., systematic reviews) of data in tables from publications and clinical trial studies. We describe a important component of a system to automatically produce evidence reports that performs two key functions: (i) understanding the meaning of data in medical tables and (ii) identifying and retrieving relevant tables given a input query. We present modifications to our existing framework for inferring the semantics of tables and an ontology developed to model and represent medical tables in RDF. Representing medical tables as RDF makes it easier for the automatic extraction, integration and reuse of data from multiple studies, which is essential for generating meta...
Yearbook of Medical Informatics, 2020
Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions fo...