Abstract
Long-term management of chronic disorders such as Type 2 Diabetes (T2D) requires personalised care for patients due to variation in patient characteristics and their response to a specific line of treatment. The availability of large volumes of electronic records of T2D patient data provides opportunities for application of big data analysis to gain insights into the disease manifestation and its impact on patients. Data science in healthcare has the potential to identify hidden knowledge from the database, re- confirm existing knowledge, and aid in personalising treatment. In this paper, we present a suite of data analytics for T2D disease management that allows clinicians and researchers to identify associations between different patient biological markers and T2D related complications. The analytics suite consists of exploratory, predictive, and visual analytics with capabilities including multi-tier classification of T2D patient profiles that associate them to specific conditions, T2D related complication risk prediction, and prediction of patient response to a particular line of treatment. The analytics presented in this paper explore advanced data analysis techniques, which are potential tools for clinicians in decision-making that can contribute to better management of T2D.
Highlights
The rapid technological advancements in cloud technologies, big data infrastructure, and artificial intelligence have generated significant excitement in developing data-driven solutions for various domains including the healthcare sector
The project AEGLE, commissioned by European Union (EU), developed a big data framework aimed at providing big data services for healthcare, including electronic healthcare record data storage, data analytics, cloud services for accelerated training of complex analytics, and real-time processing of large data volumes
We present our work on data analysis of Type 2 Diabetes (T2D) data designed for finding associations between different patient markers, risk predictions for various complications, and prediction of patient response to medications
Summary
The rapid technological advancements in cloud technologies, big data infrastructure, and artificial intelligence have generated significant excitement in developing data-driven solutions for various domains including the healthcare sector. We present our work on data analysis of T2D data designed for finding associations between different patient markers, risk predictions for various complications, and prediction of patient response to medications. The principle approach followed for the T2D data analysis was to cluster the patients according to their demographics and biological markers and investigate their associations with known T2D related complications followed by the development of predictive analytics to FIGURE 3. The classifier aims to classify patients according to a preferred demographic category (e.g., age or gender) and a biological marker class (e.g., HbA1c levels) and associate the classes to a complication (e.g., blindness) This multi-tier classification is achieved by means of a population pyramid analytic that helps to understand the composition of the population according to chosen criteria [21]. The survival time of the patients is computed for the patients from the time of diabetes diagnosis to the occurrence of the complication
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