scholarly journals A Case Study of the Impact on Postprandial Plasma Glucose Based on the 14-Day Sensor Device Reliability (GH-Method: Math-Physical Medicine)

2020 ◽  
Keyword(s):  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sensor Device ◽  
Device Reliability ◽  
The Impact

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 the Impact of Different Intensity of Exercise on Postprandial Plasma Glucose (No. 65)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Data Set ◽  
Postprandial Plasma Glucose ◽  
Metabolic Conditions ◽  
Intensity Of Exercise ◽  
The Impact

Introduction The data-set is provided by the author, who uses his own type 2 diabetes (T2D) 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 A1C Variance Study and PPG Prediction Methodology over Six Periods Using GH-Method: Math-Physical Medicine

2020 ◽  
Vol 5 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Hemoglobin A1c ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Index Weight ◽  
Engineering Modeling

In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances based on six periods data utilizing the GH-Method: math-physical medicine approach by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI). He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C. All prediction models have reached to 95% to 99% accuracy. His focus is on preventive medicine, especially on diabetes control via lifestyle management.

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

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Plasma Glucose ◽  
Test Strip ◽  
Prediction Methods ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
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.

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

2020 ◽  
Vol 5 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Medical Research ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
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.

A biophysical investigation on the relationships between the inputs of meal quantity percentage, carbs/sugar amount, and exercise, along with the outputs of postprandial plasma glucose, and body weight using GH-Method: Math-physical medicine (No. 295)

2020 ◽  
Keyword(s):  
Body Weight ◽  
Plasma Glucose ◽  
Influential Factors ◽  
Research Note ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
The Relationship

This research note describes the author’s investigation on differences among his three meals, which include breakfast, lunch, and dinner, in terms of their influential factors and their respective PPG data and waveforms. He further described the relationship between his body weight and meal quantity percentage for his normal portion. During this period, from 5/5/2018 to 7/14/2020, he collected detailed information of his 2,403 meals and ~64,000 glucose data.

scholarly journals Coefficient of GH.F-Modulus in the Linear Elastic Fasting Plasma Glucose Behavior Study Based on Health Data of Three Diabetes Patients Using the GH-Method: Math-Physical Medicine, Part 4 (No. 356)

2020 ◽  
pp. 1-7
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Fasting Plasma ◽  
Behavior Study ◽  
Diabetes Patients

This article is Part 4 of the author’s linear elastic glucose behavior study, which focuses on fasting plasma glucose (FPG) component. It is the continuation of his previous three studies, Parts 1, 2, and 3, on linear elastic postprandial plasma glucose (PPG) behaviors

scholarly journals Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)

2021 ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Average ◽  
Fasting Plasma ◽  
Average Glucose

This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020

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