A big data framework for diabetes in Mauritius

Telemedicine, Electronic Health Records (EHR) and social media seem to have shown promising directions to deal with diabetes. With the rapid advances in ICT, various diabetes information systems have evolved in the past years. The availability of new technologies for diagnosing, monitoring and treating diabetes is critical to achieve recommended metabolic control. Additionally, Big Data solutions are necessary to meet the massive transformation currently occurring in the healthcare industry. The paper highlights the current status of diabetes in Mauritius. Additionally it analyses some existing solutions for diabetes management. The paper also discusses the challenges of Big Data in healthcare and proposes a Big Data framework for diabetes mellitus in Mauritius.

[1]  F. Parazzini,et al.  The outpatient cost of diabetes care in Italian diabetes centers. , 2001, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[2]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[3]  Yuqing Zhu,et al.  BigDataBench: A big data benchmark suite from internet services , 2014, 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA).

[4]  Riccardo Bellazzi,et al.  Management of patients with diabetes through information technology: tools for monitoring and control of the patients' metabolic behavior. , 2004, Diabetes technology & therapeutics.

[5]  Rajkumar Buyya,et al.  The anatomy of big data computing , 2015, Softw. Pract. Exp..

[6]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..

[7]  Lisa Hartling,et al.  Social media use among patients and caregivers: a scoping review , 2013, BMJ Open.

[8]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[9]  Anatoly I. Petrenko,et al.  Mobile health applications to support diabetic patient and doctor , 2014, Proceedings of IEEE East-West Design & Test Symposium (EWDTS 2014).

[10]  Andrea De Mauro,et al.  A formal definition of Big Data based on its essential features , 2016 .

[11]  G. Joshy,et al.  Diabetes information systems: a rapidly emerging support for diabetes surveillance and care. , 2006, Diabetes technology & therapeutics.

[12]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[13]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[14]  Diabetes Professionals Must Seize the Opportunity in Mobile Health , 2013, Journal of diabetes science and technology.

[15]  Emad A. Mohammed,et al.  Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends , 2014, BioData Mining.

[16]  Gregory B. Cline,et al.  Information technology systems in public sector health facilities in developing countries: the case of South Africa , 2013, BMC Medical Informatics and Decision Making.

[17]  H. Parihar,et al.  iOS Appstore-Based Phone Apps for Diabetes Management: Potential for Use in Medication Adherence , 2017, JMIR diabetes.

[18]  E. Ewen,et al.  Electronic health record use to classify patients with newly diagnosed versus preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management. , 2012, Population health management.

[19]  Jorge Bernardino,et al.  Big Data Issues , 2015, IDEAS.

[20]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[21]  David J. Faulds,et al.  Social media: The new hybrid element of the promotion mix , 2009 .

[22]  W. Rutala,et al.  Healthcare-associated infections , 2016 .

[23]  J. Shaw,et al.  Explaining the Increase of Diabetes Prevalence and Plasma Glucose in Mauritius , 2011, Diabetes Care.

[24]  Jeffrey L. Brudney,et al.  Coproduction of Government Services and the New Information Technology: Investigating the Distributional Biases , 2013 .

[25]  Domenico Talia,et al.  P2P-MapReduce: Parallel data processing in dynamic Cloud environments , 2012, J. Comput. Syst. Sci..

[26]  Patricia Salber,et al.  Digital Health Tools for Diabetes , 2015, The Journal of ambulatory care management.

[27]  P Zimmet,et al.  International Diabetes Federation: a consensus on Type 2 diabetes prevention , 2007, Diabetic medicine : a journal of the British Diabetic Association.

[28]  Peter Groves,et al.  The 'big data' revolution in healthcare: Accelerating value and innovation , 2016 .

[29]  Bin Jiang,et al.  Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.