Using Time-Series and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)

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Abstract

This paper describes the author’s application of Time-Series Analysis and forecasting to manage type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own T2D metabolic conditions 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 the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.
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使用时间序列和预测来管理2型糖尿病(GH-Method:数学-物理医学)
本文介绍了作者将时间序列分析和预测应用于2型糖尿病(T2D)的情况。数据集由作者提供,他使用自己的T2D代谢条件控制,作为一个案例研究,通过“数学-物理医学”方法在医学研究中的非传统方法。数学物理医学(MPM)从观察人体的物理现象(不是生物或化学特征)开始,收集与疾病相关的数据元素(更倾向于大数据),利用适用的工程建模技术,建立适当的数学方程(不仅仅是统计分析),最后预测疾病的发展方向和控制机制。
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