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Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.

ABSTRACT: INTRODUCTION:A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ontologies by complementing expert knowledge with knowledge automatically extracted from clinical practice guidelines and electronic health records, which minimizes reliance on expensive domain expertise for gold standards generation. METHODS:We developed a bacterial clinical infectious diseases ontology (BCIDO) to assist clinical infectious disease treatment decision support. Using a semi-automated method we integrated diverse knowledge sources, including publically available infectious disease guidelines from international repositories, electronic health records, and expert-generated infectious disease case scenarios, to generate a compendium of infectious disease knowledge and use it to evaluate the accuracy and coverage of BCIDO. RESULTS:BCIDO has three classes (i.e., infectious disease, antibiotic, bacteria) containing 593 distinct concepts and 2345 distinct concept relationships. Our semi-automated method generated an ID knowledge compendium consisting of 637 concepts and 1554 concept relationships. Overall, BCIDO covered 79% (504/637) of the concepts and 89% (1378/1554) of the concept relationships in the ID compendium. BCIDO coverage of ID compendium concepts was 92% (121/131) for antibiotic, 80% (205/257) for infectious disease, and 72% (178/249) for bacteria. The low coverage of bacterial concepts in BCIDO was due to a difference in concept granularity between BCIDO and infectious disease guidelines. Guidelines and expert generated scenarios were the richest source of ID concepts and relationships while patient records provided relatively fewer concepts and relationships. CONCLUSIONS:Our semi-automated method was cost-effective for generating a useful knowledge compendium with minimal reliance on domain experts. This method can be useful for continued development and evaluation of biomedical ontologies for better accuracy and coverage.

SUBMITTER: Gordon CL

PROVIDER: S-EPMC4724344 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.

Gordon Claire L CL Weng Chunhua C

Journal of biomedical informatics 20150723

<h4>Introduction</h4>A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ontologies by complementing expert knowledge with knowledge automatically extracted from clinical practice guidelines and electronic health records, which minimizes reliance on expensive domain expertise for gold standards generation.<h4>Metho ...[more]

PMID: 26212414

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Project description:The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.

Project description:ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Dataset Information

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.

Publications

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.

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