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 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.

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 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 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 Applying Segmentation Pattern Analysis to Investigate Postprandial Plasma Glucose Characteristics and Behaviors of the Carbs/Sugar Intake Amounts In Different Nations (GH Method: Math-Physical Medicine)

2020 ◽  
Vol 5 (1) ◽  
Keyword(s):  
Plasma Glucose ◽  
Pattern Analysis ◽  
Communication Model ◽  
Physical Medicine ◽  
Internal Organs ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Liver And Pancreas ◽  
Segmentation Pattern ◽  
The Brain

In this paper, the author presents the results of his national segmentation pattern analysis of the sensor PPG data based on both high-carb and low-carb intake amounts. It also verified his earlier findings on the communication model between the brain and internal organs such as the stomach, liver, and pancreas.

scholarly journals Using GH-Method: Math-Physical Medicine, Mentality-Personality Modeling, and Segmentation Pattern Analysis to Compare Two Clinic Cases About Linkage Between T2d Patient’s Psychological Behavior and Physiological Characteristics

2020 ◽  
Vol 5 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Personality Traits ◽  
Pattern Analysis ◽  
Physiological Characteristics ◽  
Physical Medicine ◽  
Specific Question ◽  
Psychological Behavior ◽  
Segmentation Pattern

Introduction The Author Has Contemplated a Specific Question Why do some type 2 diabetes (T2D) patients choose to face serious complications, including death, rather than change their lifestyle in order to control their diabetic conditions? This paper utilized segmentation pattern analysis to analyze two different clinic cases linking T2D patient’s personality traits and psychological behavior with diabetes physiological characteristics.

scholarly journals Using Candlestick Charting and GH-Method: Math-Physical Medicine to Investigate the Differences of Glucose Measurement Results Based on both Finger-Piercing Method and Continuous Monitoring Device

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Continuous Monitoring ◽  
Big Data Analytics ◽  
Glucose Measurement ◽  
Physical Medicine ◽  
Continuous Glucose Monitor ◽  
Segmentation Analysis ◽  
Measurement Results ◽  
Engineering Modeling ◽  
Monitor Device ◽  
Metabolism Model

The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science such as big data analytics and artificial intelligence to derive the mathematical metabolism model and three prediction tools for weight, FPG, and PPG with >30 input elements. This research paper describes glucose measurement results based on the finger-piercing method and continuous glucose monitor device using candlestick charting and segmentation analysis.

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