4th International Conference on Multidisciplinary Studies in Health Sciences, Antalya, Turkey, 19 - 20 February 2022, pp.1
Diabetes mellitus (DM) is a common cause of morbidity and mortality worldwide. The prevalence of DM is growing year after year. It is estimated that 700 million people worldwide will develop DM by 2045. According to the data of the International Diabetes Federation (IDF), the prevalence of DM in 2021 was found to be 10.5% worldwide and 14.5% in Turkey. It is predicted that diabetes will be the seventh leading cause of death worldwide by 2030. Today, an increasingly growing amount of data is collected about individuals. Activity trackers are an example of wearable devices that collect and compile health data. The routine collection of large amounts of data is the base of Big Data. The ubiquity of smartphones with significant information processing power provides an opportunity to benefit from digital health technology easily and inexpensively. Big data sources, such as electronic health records, smart glucose meters, insulin pumps and automatic insulin delivery system, data kept by the patient, mobile phone applications related to diabetes, digital images from retinal scans, are used in big data research related to diabetes. Machine learning (ML) techniques of artificial intelligence (AI) can be used to predict insulin requirements and hypoglycemia episodes. ML can also be used to analyze retinal images. The volume of data on glycemic control will increase to a great extent as more people with diabetes connect toautomated glucose sensors and apps that continuously measure interstitial glucose. Clinicians will not only be able to assess long-term control through HbA1c but will be able to assess minute-to-minute glucose levels. Smart pills with a chip will be able to signal when the person takes their medicine. This will ensure better categorization of those at risk of DM and taking more targeted preventive measures. AI created by using big data will power applications that provide personalized guidance, such as adjusting treatment regimens and dietary recommendations for people with diabetes. Increased use of real-world data will ensure that the evidence applies to individuals with DM. Factors specific to a particular ethnic group or place of residence will be quickly identified. Big data use and analysis has great potential for cost effectiveness. Big data should be used to the maximum extent to develop new insights.