Abstract of Paper

Title: Using Quantitative Medicine to Control Type 2 Diabetes
By: Gerald C. Hsu, eclaireMD Foundation
Date: November 28, 2016

The author has had long-term chronic diseases and suffered from type 2 diabetes, hyperlipidemia, and hypertension for a period of 20 years.  His primary health data in 2010 are listed as follows:
Weight:205 lbs.
Waistline:44 inches
One-time snap check of PPG:350 mg/dL
90-days of averaged glucose:280 mg/dL
ACR:116 mg/mmol
Triglycerides:1161 mg/dL

The author spent 7 years (2010-2017) conducting research to find an effective way to control his severe diabetic condition.  As a result, he developed three mathematical models and various tools to control his chronic diseases, with an emphasis on type 2 diabetes.  During this process, he collected approximately one million “clean” data regarding his health condition.  He applied multiple disciplines, including advanced mathematics, computer science (e.g. database, big data analytic, cloud computing, and mobile technology), nonlinear and dynamic digitized engineering modeling, and artificial intelligence or “AI” (automation and machine learning) to simulate the human organic metabolic system.

The author created two prediction models of weight and glucose values in order to provide patients with an early warning to alter their lifestyle.  The glucose prediction model includes factors such as diabetes medication consumed, quality of food and meal, type of dining location (home or restaurant), exercise amount, stress and trauma, current residence location and weather condition, traveling category and frequency, decreasing internal organ function, and time delay impact on glucose measurement.  The weight prediction model includes factors such as quantity of food and meal, type of exercise, change in calories, sleep impact, water consumption, and other prominent factors.

The most significant achievement is that he successfully eliminated the use of all his diabetic medications within the past 2 years (2016-2017), while maintaining his A1C level within the range of 6.2% to 6.6%. In addition, his hypertension and hyperlipidemia came under control.

His recent summarized health data is listed below:
Weight:172 lbs.
Waistline:32 inches
Averaged 90-days glucose:115 mg/dL
A1C:6.3 – 6.5% (without any medication)
ACR:12.6 mg/mmol

His entire research and development efforts have been based on lifestyle management as a part of preventive medicine, using collection, processing, and analysis of quantitative medical and health data.  The results are displayed in 75 figures and diagrams which have hundreds to thousands of data within each illustration.  This paper has indicated many conclusive correlations among 11 categories. These categories include four health outputs (weight and waistline, glucose, blood pressure, lipid), six health inputs (food and meal, exercise, stress, sleep, water drinking, life pattern regularity), and time effect.  All of these 11 categories are composed of approximately 500 elements. All of them are carefully monitored by special-designed computer software via smartphone or personal computer.  About 95% of these elements are managed by AI in which 20 to 25 elements are required by the patient’s daily input.  The metabolism indices for the past 5 years are highly consistent with the author’s health state found from various lab test results.  The accuracy of weight prediction and glucose prediction has reached 99.9% and 99.0%, respectively.

The author’s findings of correlations and conclusions are highly consistent with the commonly available understandings within the medical community.  There is no personal prejudice inside this study as it is based on experimental facts.  Therefore, the author hopes that the same existing medical conclusions can be further backed up and proven by a scientific big data analytics approach.

The phase 2 of this project will include the following:
(1) Collecting and presenting data from mass population of worldwide patients with type 2 diabetes and;
(2) Researching and developing more effective ways to influence or alter patients’ health behavior to adapt a better lifestyle management.