Methodology of Math-Physical Medicine (GH-Method)

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.

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

Advancements in Journal of Urology and Nephrology ◽

10.33140/ajun.02.02.03 ◽

2020 ◽

Vol 2 (2) ◽

Keyword(s):

Type 2 Diabetes ◽

Time Series ◽

Physical Medicine ◽

Related Data ◽

Metabolic Conditions ◽

Mathematical Equations ◽

Engineering Modeling ◽

And Control ◽

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.

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

Advances in Bioengineering and Biomedical Science Research ◽

10.33140/abbsr.03.01.08 ◽

2020 ◽

Vol 3 (1) ◽

Keyword(s):

Relative Energy ◽

Physical Medicine ◽

Related Data ◽

Segmentation Pattern ◽

Metabolic Conditions ◽

Research Findings ◽

Mathematical Equations ◽

Engineering Modeling ◽

And Control ◽

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

Advances in Theoretical & Computational Physics ◽

10.33140/atcp.03.02.02 ◽

2020 ◽

Vol 3 (2) ◽

Keyword(s):

Control Mechanism ◽

Chemical Characteristics ◽

Physical Medicine ◽

Related Data ◽

Modeling Techniques ◽

Mathematical Equations ◽

Engineering Modeling ◽

And Control ◽

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.

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

Journal of Clinical & Experimental Immunology ◽

10.33140/jcei.05.03.01 ◽

2020 ◽

Vol 5 (3) ◽

Keyword(s):

Relative Energy ◽

Physical Medicine ◽

Related Data ◽

Segmentation Pattern ◽

Metabolic Conditions ◽

Research Findings ◽

Mathematical Equations ◽

Engineering Modeling ◽

And Control ◽

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

10.47890/jnshd/2020/geraldchsu/10243805 ◽

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 ◽

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.

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

Medical & Clinical Research ◽

10.33140/mcr.05.03.02 ◽

2020 ◽

Vol 5 (3) ◽

Keyword(s):

Type 2 Diabetes ◽

Big Data ◽

Plasma Glucose ◽

Physical Medicine ◽

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.

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

Advances in Bioengineering and Biomedical Science Research ◽

10.33140/abbsr.03.03.02 ◽

2020 ◽

Vol 3 (3) ◽

Keyword(s):

Type 2 Diabetes ◽

Medical Research ◽

Plasma Glucose ◽

Fasting Plasma Glucose ◽

Physical Medicine ◽

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.

Using GH-Method: Math-Physical Medicine to Conduct the Accuracy Comparison of Two different Postprandial Plasma Glucosa Prediction Methods

Advances in Theoretical & Computational Physics ◽

10.33140/atcp.03.02.03 ◽

2020 ◽

Vol 3 (2) ◽

Cited By ~ 1

Keyword(s):

Type 2 Diabetes ◽

Plasma Glucose ◽

Test Strip ◽

Prediction Methods ◽

Physical Medicine ◽

Accuracy Comparison ◽

Metabolic Conditions

This paper describes the accuracy of two different methods of postprandial plasma glucose (PPG) prediction in comparison with the actual measured PPG by using the finger-piercing and test-strip (Finger) method. 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.

Type 2 Diabetes Nursing Guidelines based on Wave and Energy Theories Associated with Three Distinctive Postprandial Plasma Glucose Waveforms (GH-Method: Math-Physical Medicine)

Journal of Nursing & Healthcare ◽

10.33140/jnh.05.02.03 ◽

2020 ◽

Vol 5 (2) ◽

Keyword(s):

Type 2 Diabetes ◽

Medical Research ◽

Plasma Glucose ◽

Physical Medicine ◽

Metabolic Conditions ◽

Nursing Guidelines ◽

Diabetes Nursing

This paper summarizes tips and guidelines of optimized combination of food and exercise for type 2 diabetes (T2D) patients to control their glucoses. 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.