Big Data - Smart Health Strategies

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

• References

• 1 Laney D. 3D data management: controlling data volume, velocity and variety. Available at: blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Retrieved: May 2014
• 2 Martin-Sanchez F, Verspoor K. Big data in medicine is driving big changes. Yearb Med Inform 2014; 14-20.
• 3 Bahga A, Madisetti VK. A cloud-based approach for interoperable Electronic Health Records (EHRs). IEEE J Biomed Health Inform 2013; 17 (Suppl. 05) 894-906.
• 4 Xia H, Asif I, Zhao X. Cloud-ECG for real time ECG monitoring and analysis. Comput Methods Programs Biomed 2013; Jun 110 (Suppl. 03) 253-9.
• 5 Bourgeat P, Dore V, Villemagne VL, Rowe CC, Salvado O, Fripp J. MilxXplore: a web-based system to explore large imaging datasets. J Am Med Inform Assoc 2013; Nov-Dec 20 (Suppl. 06) 1046-52.
• 6 Post AR, Kurc T, Cholleti S, Gao J, Lin X, Bornstein W. et al. The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data. J Biomed Inform 2013; Jun 46 (Suppl. 03) 410-24.
• 7 Song M, Yang H, Siadat SH, Pechenizkiy M. A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Syst Appl 2013; Jul 40 (Suppl. 09) 3722-37.
• 8 Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT. et al. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One 2013 Dec 2 8 (Suppl. 12) e80503.
• 9 Alonso F, Lara JA, Martinez L, Pérez A, Valente JP. Generating reference models for structurally complex data. Methods Inf Med 2013; 52 (Suppl. 05) 441-53.
• 10 McGregor C. Big data in neonatal intensive care. IEEE Computer 2013; Jun 46 (Suppl. 06) 54-9.
• 11 Srinivasan U, Arunasalam B. Leveraging big data analytics to reduce healthcare costs. IEEE IT Professional 2013; Nov-Dec 15 (Suppl. 06) 21-8.
• 12 Teodoro D, Lovis C. Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends. PLoS One 2013 Apr 25 8 (Suppl. 04) e61180.
• 13 LePendu P, Iyer SV, Bauer-Mehren A, Harpaz R, Mortensen JM, Podchiyska T. et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther 2013; Jun 93 (Suppl. 06) 547-55.
• 14 Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics 2013; Dec 69 (Suppl. 04) 893-902.
• 15 Bellazzi R. Big data and biomedical informatics: a challenging opportunity. Yearb Med Inform 2014; 8-13.
• 16 Peek N, Holmes J, Sun J. Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics. Yearb Med Inform 2014; 42-7.
• 17 O’Driscoll A, Daugelaite J, Sleator RD. ‘Big Data’, Hadoop and cloud computing in genomics. J Biomed Inform 2013; Oct 46 (Suppl. 05) 774-81.