Big Data - Smart Health Strategies

V. Koutkias; F. Thiessard; Section Editors for the IMIA Yearbook Special Section

doi:10.15265/IY-2014-0031

Yearbook of Medical Informatics, Inhaltsverzeichnis

Yearb Med Inform 2014; 23(01): 48-51
DOI: 10.15265/IY-2014-0031

Original Article

Georg Thieme Verlag KG Stuttgart

Big Data - Smart Health Strategies

Findings from the Yearbook 2014 Special Theme

V. Koutkias

¹INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), F-93430, Villetaneuse, France

,

F. Thiessard

²Univ. Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, Equipe de Recherche en Informatique Appliquée à la Santé, F-33000 Bordeaux, France

,

Section Editors for the IMIA Yearbook Special Section

› Institutsangaben

Abstract

Summary

Objectives: To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts.

Methods: A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field.

Results: The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs.

Conclusions: The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.

Keywords

Big data - cloud computing - personalized medicine - smart health strategies

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Referenzen

References
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