Big Data Technologies

Abstract

The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.

In Search of Big Medical Data Integration Solutions - A Comprehensive Survey

“…In MOSAIC system, the i2b2 [164] Data Warehouse (DW) allows integrating clinical information coming from hospital EHRs, administrative data from the local health care agencies, and environmental data collected from satellites. A common data model was defined and implemented using the i2b2 technology to query and integrate these heterogeneous huge data, [165]:…”

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

“…This will provide information on the context of CGM measurement, potentially driving to a better understanding of diabetes and the environmental factors that can influence its onset/course [101,102].…”

“…Their processing by machine learning, datamining, and big data analytics [101,104], however, is sensitive to the reliability of data sources [101,104]. In fact, among the "V" properties of big data [105], that indicated as veracity refers to the uncertain nature of data collected in large volumes with limited control of the quality.…”

Artificial intelligent-based personalized predictive ischemic stroke among type 2 diabetes mellitus complication patients

“…Healthcare providers can use this information to take preventative measures and reduce the risk of stroke in high-risk patients. However, further exploration is required to ensure the accuracy and applicability of the model to other patient populations.complications (Bellazzi et al, 2015;Ellahham, 2020;Fagherazzi & Ravaud, 2019). machine learning proposes a complementary method to benchmark prediction modeling that may tackle existing issues (Bellazzi et al, 2015;Kerr & Klonoff, 2019).…”

“…However, further exploration is required to ensure the accuracy and applicability of the model to other patient populations.complications (Bellazzi et al, 2015;Ellahham, 2020;Fagherazzi & Ravaud, 2019). machine learning proposes a complementary method to benchmark prediction modeling that may tackle existing issues (Bellazzi et al, 2015;Kerr & Klonoff, 2019). It can improve medicine by better taking advantage of "big data for algorithm development."…”