Akademik Veri Yönetim Sistemi
4th International Conference on Multidisciplinary Studies in Health Sciences, Antalya, Türkiye, 19 - 20 Şubat 2022, ss.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.