Efficient Results in Semantic Interoperability for Health Care

L. F. Soualmia; J. Charlet; Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

doi:10.15265/IY-2016-051

Yearbook of Medical Informatics, Table of Contents

Yearb Med Inform 2016; 25(01): 184-187
DOI: 10.15265/IY-2016-051

IMIA and Schattauer GmbH

Georg Thieme Verlag KG Stuttgart

Efficient Results in Semantic Interoperability for Health Care

Findings from the Section on Knowledge Representation and Management

Authors

L. F. Soualmia

¹Normandie Universités, Univ. Rouen, NormaSTIC FR CNRS 3638, IRIB and LITIS EA 4108, Information Processing in Biology & Health, Saint Étienne du Rouvray, France

²INSERM, UMR_S 1142, LIMICS, Paris, France; Sorbonne Universités, Univ. Paris 06, Paris, France
J. Charlet

²INSERM, UMR_S 1142, LIMICS, Paris, France; Sorbonne Universités, Univ. Paris 06, Paris, France

³AP-HP, Dept. of Clinical Research and Development, Paris, France

Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

Abstract

Summary

Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain.

Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review.

Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering.

Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Keywords

Biomedical ontologies - information storage and retrieval - knowledge representation - vocabulary controlled

Full Text

References

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