A systematic literature review of continuous blood glucose monitoring and suggesting the quantity of insulin or artificial pancreas (AP) for diabetic type 1 patients

Background: Diabetes Mellitus is one of the most common diseases, which is rapidly increasing worldwide. Early detection of Blood Glucose Level not only helps in better management of Diabetes Mellitus but also decreases the cost of treatment. In the recent past, numerous researches have been carried out to monitor blood glucose level which suggests the quantity of insulin i.e. artificial pancreas. Method: In this paper, we summarize and analyze the past work of continuous blood glucose monitoring and automatic insulin suggestion, in a systematic way. Particularly, 24 journal studies from 2015 to 2018 are identified and analyzed. The paper provided a dynamic study of insulin-glucose regulators by identifying some research questions and answering from the literature. Moreover, it provides brief of the methodology of each study and how it contributes towards this field. It also underlines the advantages of the methods used in past and how they lack in determining other aspects for achieving a completely autonomous, adaptive and individualized model. Results: A comprehensive investigation of the selected studies leads to identify four major areas i.e. Machine learning techniques (8 studies), MPC (6 studies), PID (2 studies), mixed (6) and others (2 studies).Conclusion: This study is helpful in opening a gateway for new researchers to have an overview of the past work on continuous glucose monitoring and insulin suggestion. It identifies the challenges in this particular domain in order to lay the foundation for future research. The survey discovers the most popular techniques used for blood glucose monitoring and insulin suggestion, exogenous or intravenous (Subcutaneous) or artificial pancreas. For future work, the nonlinear autoregressive neural network based model predictive controller is suggested.