Open AccessUsing Time-Series and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)
Publication year - 2020
Publication title -
advancements in journal of urology and nephrology
Language(s) - English
Resource type - Journals
ISSN - 2689-8616
DOI - 10.33140/ajun.02.02.03
Subject(s) - series (stratigraphy) , computer science , time series , type (biology) , control (management) , type 2 diabetes , big data , physical system , mathematics , medicine , artificial intelligence , machine learning , diabetes mellitus , data mining , paleontology , ecology , physics , quantum mechanics , biology , endocrinology
This paper describes the author’s application of Time-Series Analysisand forecasting to manage type 2 diabetes (T2D) conditions. Thedataset is provided by the author, who uses his own T2D metabolicconditions control, as a case study via the “math-physical medicine”approach of a non-traditional methodology in medical research.Math-physical medicine (MPM) starts with the observation of thehuman body’s physical phenomena (not biological or chemicalcharacteristics), collecting elements of the disease related data(preferring big data), utilizing applicable engineering modelingtechniques, developing appropriate mathematical equations (notjust statistical analysis), and finally predicting the direction of thedevelopment and control mechanism of the disease.