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An abstraction network is a compact network summarizing the structure and content of a given ontology. Abstraction networks have been shown to support orientation into and quality assurance of ontologies. Area and partial-area taxonomies are examples of abstraction networks that utilize the relationships of an ontology to group together classes with similar(More)
The use of a top-level ontology, e.g. the Basic Formal Ontology (BFO), as a template for a domain ontology is considered a best practice. This saves design efforts and supports multi-disciplinary research. The Drug Discovery Investigations ontology (DDI) for automated drug discovery investigations followed the best practices and imported BFO. However not(More)
BioPortal contains over 300 ontologies, for which quality assurance (QA) is critical. Abstraction networks (ANs), compact summarizations of ontology structure and content, have been used in such QA efforts, typically in a "one-off" manner for a single ontology. Ontologies can be characterized-independently of knowledge-content focus-from a structural(More)
An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one(More)
OBJECTIVE To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time. METHODS Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial(More)
ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for(More)
OBJECTIVE By 2015, SNOMED CT (SCT) will become the USA's standard for encoding diagnoses and problem lists in electronic health records (EHRs). To facilitate this effort, the National Library of Medicine has published the "SCT Clinical Observations Recording and Encoding" and the "Veterans Health Administration and Kaiser Permanente" problem lists(More)
ClinicalTrials.gov has been archiving clinical trials since 1999, with > 165,000 trials at present. It is a valuable but relatively untapped resource for understanding trial design patterns and acquiring reusable trial design knowledge. We extracted common eligibility features using an unsupervised tag-mining method and mined their temporal usage patterns(More)
BACKGROUND When new concepts are inserted into the UMLS, they are assigned one or several semantic types from the UMLS Semantic Network by the UMLS editors. However, not every combination of semantic types is permissible. It was observed that many concepts with rare combinations of semantic types have erroneous semantic type assignments or prohibited(More)
OBJECTIVE To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies. METHODS We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study(More)