Yearb Med Inform 2017; 26(01): 148-151
DOI: 10.15265/IY-2017-030
Section 6: Knowledge Representation and Management
Synopsis
Georg Thieme Verlag KG Stuttgart

Knowledge Representation and Management, It’s Time to Integrate!

Findings from the IMIA Yearbook Section on Knowledge Representation and Management
F. Dhombres
INSERM, UMR_S 1142, LIMICS, 75006 Paris, France Sorbonne Universités, UPMC Univ. Paris 06, UMR_S 1142, LIMICS, 75006 Paris, France Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, 93430 Villetaneuse, France
Department of Fetal Medicine, Armand Trousseau Hospital, APHP, 75012 Paris, France
,
J. Charlet
INSERM, UMR_S 1142, LIMICS, 75006 Paris, France Sorbonne Universités, UPMC Univ. Paris 06, UMR_S 1142, LIMICS, 75006 Paris, France Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, 93430 Villetaneuse, France
AP-HP, Department of Clinical Research and Innovation, Paris, France
› Author Affiliations
Further Information

Publication History

Publication Date:
11 September 2017 (online)

Summary

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM).

Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query.

Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN).

Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development.

Section
Knowledge Representation and Management

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Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html

Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8

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