GH-METHODS

Math-Physical Medicine

NO. 008

Using GH-Method: Math-Physics Medicine to Control Type-2 Diabetes

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

Abstract
The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI) to derive the mathematical metabolism model and three prediction tools for weight, FPG, and PPG with >30 input elements. This study includes 11 categories: weight, glucose, blood pressure, lipids, food, water, exercise, sleep, stress, life pattern regularity, time with ~500 input and output elements. He collected more than 1 million “clean” data over 7 years.

Introduction
The author spent 8.5 years and 23,000 hours to research his diabetes conditions. He developed his GH-Method: math-physical medicine (MPM) 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.

The author has had type-2 diabetes (T2D), hyperlipidemia, and hypertension for 25 years. His health data in 2010 versus 2018 are listed as follows:

  • Weight: 205/170 lbs.
  • Waistline: 44/33 in.
  • FPG/PPG: 185/380 vs. 107/119 mg/dL
  • 90-days daily glucose: 279/117 mg/dL
  • A1C: 10.0%/6.5 %
  • ACR: 116/12 mg/mmol
  • Triglycerides: 1161/67 mg/dL
  • LDL/HDL: 174/28 vs. 74/48
  • SBP/DBP: 250/113 vs. 105/65 mmHG
Table 1: Health Exam Data from 2010-2017

Methods
He applied multiple nature scientific disciplines, including advanced mathematics, big data analytics, artificial intelligence, cloud mobile computing, nonlinear engineering modeling, optical physics, signal processing, wave theory, and energy theory to conduct his research for 23,000 hours in 8.5-years. He simulated the human organic metabolic system using 10 categories including four outputs (weight, glucose, blood pressure, lipid), and six inputs (food, exercise, stress, sleep, water, life pattern regularity) with ~500 elements. He defined two new terms, metabolism index (MI) and General Health State Unit (GHSU) for measuring his daily health status. He collected ~1.5 million data and developed four prediction models with more than 20 influential factors, Weight, FPG, PPG, A1C, to provide early estimation and warning.

He performed statistical analyses, including time-series, spatial analysis, frequency domain, for durations between 1,280 and 1,825 days with about 30,000 – 90,000 data to identify basic characteristics of glucose formation and prediction. Primary factors, such as medication, weight, carbs & sugar, exercise, and weather contribute about 90% of glucose formation. Secondary factors, such as measurement of time, stress, sleep, illness, and traveling provide the remaining 10% of the glucose formation.

Results
The predicted results for weight, FPG, and PPG have reached >99% linear accuracy and >80% correlation with actual data. The predicted A1C has >95% accuracy rate due to the 5% to 10% safety margin.

Conclusion
The author applied GH-Method: math-physical medicine, which is a scientific and quantitative lifestyle management method, to successfully control his T2D conditions.

Figure 1: Average Glucose from 4/1/17 – 8/31/17
Figure 2: Average Glucose from 9/1/17 – 1/28/18

References

  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
  2. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  3. Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
  4. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.