Using Quantitative Medicine to Control Type 2 Diabetes


Section 2: Methodology

Date: 10/21/2016 17:00

During the late part of 2010, I realized that I had been totally ignorant in the area of chronic diseases, even though I was very well educated in other areas (having studied 7 different fields at various colleges over 17 years).  Therefore, I decided to dedicate my efforts on acquiring the needed “knowledge” to control and improve my health conditions.  During the initial 2-year period (2010-2011), I studied internal medicine, with a special interest in 6 chronic diseases which were diabetes, hypertension, hyperlipidemia, heart disease, stroke, and obesity. For the next 2 years (2012-2013), I focused on food science and nutrition.

After finishing those 4 years of self-studying and preparation, I was ready to fully address my health problems.  I thought about starting with traditional medical research methodology, i.e. basic research, starting from the “cellular” level.  However, I did not have sufficient financial resources and professional knowledge to go down that route.  I also believed the most difficult hurdle was my age and severity of diseases.  I might not have enough time to go through that route.  Therefore, I took stock of my strengths, which were mathematics, computer science, physics, and various engineering disciplines.  I have never received any formal training in the biomedical area.  I also decided to use the nonlinear dynamic structural engineering modeling and Finite Element Method concept of digitized engineering, both with inorganic materials, to simulate the human body’s organic metabolism system.  And then, I applied advanced mathematics to conceive its hypothesis and develop the governing equation.  I defined 4 inter-connected body output categories of weight, glucose, lipid, blood pressure; and 6 inter-connected body input categories of food, exercise, stress, sleep, water hydration, and regular life pattern for longevity.  These 10 categories contain around 500 detailed elements.  For example, just “Stress” category contains 33 different stressors for both “normal” persons (their stress comes from inter-personal social relationships) and “abnormal” persons (e.g. “personality disorders,” their stress are mostly self-induced).  I also included “Time” as the 11th category since human body conditions evolve over time, i.e. “Dynamic”.  The human body’s organic characteristics must be dealt with using Artificial Intelligence (AI), through trial-and-error and other computational techniques.  In the modeling process, I excluded all environmental factors such as pollution, radiation, toxic chemicals, poison, hormonal therapy, viral infection, and others due to their complexity and the difficulty of data collection.  These factors are important for cancer which is also a chronic disease.  It should be noted that my research is focused on preventive medicine; therefore, drugs used for treatment is only included as a part of the glucose prediction tool since it does contribute to glucose readings.

Given the average 3-4 months lifespan of blood cells, which carry glucose and lipids throughout human body, I defined the data collected during the first 3-month period as the initial conditions for solving these mathematical governing equations.  Therefore, it is important for any patient to use my tool to collect his/her initial 3-months data as complete as possible.  After applying these initial conditions on the mathematical system, I can then start to “solve the equation” (in a mathematical sense), and afterwards, the system starts to learn by itself through Artificial Intelligence (AI).  In 2014 and 2015, I began building this “organic” biomedical mathematical model and two practical prediction models for both weight (after one night of sleep) and post-meal glucose levels (2 hours after a meal) by using AI.  On January 1, 2012, I started collecting my own body health data and used that data to continuously test and improve my mathematical system. To date, I have collected and processed near 1 million “clean” data on myself.  Without including AI in system capabilities, the human mind would not be able to deal with such a large and complicated database.  As shown in Figures 2-1 and 2-2, I have reached a 99% accuracy on body weight prediction (4/11/2015-7/25/2017) and a 97% to 98% accuracy on glucose prediction (6/1/2015-7/25/2017).

Figure 2-1: Predicted and Actual Body Weight (4/11/2015 – 7/20/2017)
Figure 2-2: Predicted and Actual Daily Average Glucose (6/1/2015 – 7/25/2017)

Using my iPhone APP “Tool”, I can easily manage the massive health data over the cloud, predict my vital signs such as weight and glucose, and to monitor my overall health status via the Metabolism Index (MI) and General Health Status Unit (GHSU), which is defined as a three-month running average of the MI value.  From the chart, by mid-2014 (both MI and GHSU had dropped below the dividing health level of 73.5% for my specific case), it was clear that my overall health conditions has improved significantly (as of now, my MI and GHSU are at around 55%) through a better lifestyle management.  My “break-even” line at 73.5% is due to the set of my personal goals of weight, glucose, blood pressure, exercise, meal, sleep, etc.  See Figure: 2-3 below

Figure 2-3: Metabolism Index (MI) & General Health Status Unit (GHSU)