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.
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.
Entities:
Keywords:
Big data; cloud computing; personalized medicine; smart health; strategies
Authors: Shawn E Simpson; David Madigan; Ivan Zorych; Martijn J Schuemie; Patrick B Ryan; Marc A Suchard Journal: Biometrics Date: 2013-10-11 Impact factor: 2.571
Authors: Andrew R Post; Tahsin Kurc; Sharath Cholleti; Jingjing Gao; Xia Lin; William Bornstein; Dedra Cantrell; David Levine; Sam Hohmann; Joel H Saltz Journal: J Biomed Inform Date: 2013-02-09 Impact factor: 6.317
Authors: P LePendu; S V Iyer; A Bauer-Mehren; R Harpaz; J M Mortensen; T Podchiyska; T A Ferris; N H Shah Journal: Clin Pharmacol Ther Date: 2013-03-04 Impact factor: 6.875
Authors: Uma S Mudunuri; Mohamad Khouja; Stephen Repetski; Girish Venkataraman; Anney Che; Brian T Luke; F Pascal Girard; Robert M Stephens Journal: PLoS One Date: 2013-12-02 Impact factor: 3.240