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

2020 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Time Series ◽  
Physical Medicine ◽  
Related Data ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

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.

scholarly journals Using Time-Series and Spatial Analysis (Math-Physical Medicine) to Investigate the Relationships among Weight, Blood Pressure,Glucose, Food and Exercise

2020 ◽  
Keyword(s):  
Physical Medicine ◽  
Related Data ◽  
Modeling Techniques ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

The author uses his own type 2 diabetes metabolic conditions control as a case study using 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.

scholarly journals Methodology of Math-Physical Medicine (GH-Method)

2020 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Physical Medicine ◽  
Related Data ◽  
Modeling Techniques ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper discusses the “math-physical medicine (MPM)” approach of a non-traditional methodology in medical research. The author uses his own type 2 diabetes (T2D) metabolic conditions control as a case study for some detailed illustration and explanation of this methodology. Math-physical medicine 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.

scholarly journals Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated With Glucose

2020 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Relative Energy ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Research Findings ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional 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.

scholarly journals Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated with Glucose

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Relative Energy ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Research Findings ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional 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.

Using Candlestick Charting, Segmentation Pattern Analysis, and GHMethod: Math-Physical Medicine to calculate Effective Glucoses based on Finger-Piercing Glucose Measurement

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
C Gerald Hsu
Keyword(s):  
Pattern Analysis ◽  
Glucose Measurement ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Calculation Results ◽  
Metabolic Conditions ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper describes glucose measurements and their extensive calculation results over a period of 7.5 years based on Finger-Piercing Data (Finger) using both candlestick charting and glucose segmentation pattern analysis. The dataset is provided by the author, who uses his own type 2 diabetes 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.

scholarly journals Using a Modified Candlestick Charting Technique (OHCA) and the Synthesized PPG Waveform to Develop Two Simple Formulas for PPG Prediction (GH-Method: Math-Physical Medicine)

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Control Mechanism ◽  
Chemical Characteristics ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Related Data ◽  
Modeling Techniques ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper describes development of simple formulas for postprandial plasma glucose (PPG) prediction based on sensor-monitored data using a modified candlestick charting technique. The GH-Method: 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.

scholarly journals Using Three Analysis Methods: Time-Series, Spatial, And Frequency-Domain to Analyze and Forecast Key Diabetes Biomedical Variables (GH-Method: MathPhysical Medicine)

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Time Series ◽  
Frequency Domain ◽  
Wave Theory ◽  
Frequency Domain Analysis ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Key Variables

This paper describes the author’s application of Time-Series Analysis, Spatial Analysis, and Frequency-Domain Analysis (including wave theory, Candlestick charting, segmentation pattern analysis) to analyze and forecast certain biomedical key variables of type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

scholarly journals Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Weather Temperatures on Glucose (Both FPG And PPG)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Big Data ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Segmentation Analysis ◽  
Metabolic Conditions ◽  
The Impact

This paper is based on big data collected from a period of 1,420daysfrom 6/1/2015 to 4/21/2019 with a total of 4,260 data, including highest ambient temperature (weather) of each day in degree Fahrenheit (°F), fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) in mg/dL. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

scholarly journals Using GH-Method: Math-Physical Medicine to Investigate Different Contribution Margins of Fasting Plasma Glucose vs. Postprandial Plasma Glucose on HbA1C

2020 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Medical Research ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Fasting Plasma ◽  
Metabolic Conditions

This paper displays results of the contribution margin calculation of fasting plasma glucose (FPG) vs. postprandial plasma glucose (PPG) on HbA1C. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

scholarly journals Using Three Analysis Methods: Time-Series, Spatial, And Frequency-Domain to Analyze and Forecast Key Diabetes Biomedical Variables (GH-Method: MathPhysical Medicine)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Time Series ◽  
Frequency Domain ◽  
Wave Theory ◽  
Frequency Domain Analysis ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Key Variables

This paper describes the author’s application of Time-Series Analysis, Spatial Analysis, and Frequency-Domain Analysis (including wave theory, Candlestick charting, segmentation pattern analysis) to analyze and forecast certain biomedical key variables of type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

Sign in / Sign up

Export Citation Format

Share Document