The potential for Big Data in Type 2 diabetes

SL Hyer, J Chang, LL Chuah, R Manzoor, N Philip
<span title="2019-07-08">2019</span> <i title="Peertechz Publications Private Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wp67mwcx4vf63m6ltmsfteep5m" style="color: black;">Archives of Community Medicine and Public Health</a> </i> &nbsp;
Type 2 diabetes is a chronic condition associated with long term complications and premature death. It is a leading cause of non-traumatic amputations, renal failure and blindness. With advances in information technology, diabetes patient data are increasingly collated digitally, however, there is no unifi ed minimal dataset agreed nationally or internationally for data collection. A huge amount of data including patient data, pathology reports, and prescription information is generated by
more &raquo; ... h providers and this information collectively is known as "Big Data." The Aegle project, A European Union Horizon 2020 funded study, looks at the issue of gathering Big Data, with the aim of harnessing it to help develop personalised therapy to patients with the aim of improving health and optimise outcomes. In this paper, we highlight the potential of the Aegle Project to harness Big Data with real data examples. At the clinical decision support user interface, we show examples where predictive analysis helps stratify patients into risk categories for complications and for therapeutic responses. At the research-oriented user interface, we show how the implementation of this integrated data system has already revealed novel associations that could form the basis for future clinical studies.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17352/2455-5479.000051">doi:10.17352/2455-5479.000051</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eg276qhqvjajhg4hhwgrxd2gsu">fatcat:eg276qhqvjajhg4hhwgrxd2gsu</a> </span>
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