Journal of Clinical and Cellular Immunology
Open Access
ISSN: 2155-9899
ISSN: 2155-9899
Gerald C. Hsu
Eclaire MD Foundation, USA
Keynote: J Clin Cell Immunol
Math-physical medicine approach (MPM) utilizes mathematics, physics, engineering models, and computer science
in medical research. Initially, the author spent four years of self-studying six chronic diseases and food nutrition
to gain in-depth medical domain knowledge. During 2014, he defined metabolism as a nonlinear, dynamic, and organic
mathematical system having 10 categories with ~500 elements. He then applied topology concept with partial differential
equation and nonlinear algebra to construct a metabolism equation. He further defined and calculated two variables,
metabolism index and general health status unit. During the past 8.5 years, he has collected and processed 1.5 million
data. Since 2015, he developed prediction models, i.e. equations, for both postprandial plasma glucose (PPG) and fasting
plasma glucose (FPG). He identified 19 influential factors for PPG and five factors for FPG. He developed the PPG
model using optical physics and signal processing. Furthermore, by using both wave and energy theories, he extended
his research into the risk probability of heart attack or stroke. In this risk assessment, he applied structural mechanics
concepts, including elasticity, dynamic plastic, and fracture mechanics, to simulate artery rupture and applied fluid
dynamics concepts to simulate artery blockage. He further decomposed 12,000 glucose waveforms with 21,000 data
and then re-integrated them into three distinctive PPG waveform types which revealed different personality traits and
psychological behaviors of type 2 diabetes patients. Furthermore, he also applied Fourier Transform to conduct frequency
domain analyses to discover some hidden characteristics of glucose waves. He then developed an AI Glucometer tool for
patients to predict their weight, FPG, PPG, and A1C. It uses various computer science tools, including big data analytics,
machine learning, and artificial intelligence to achieve very high accuracy (95% to 99%).
Recent Publications
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 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.
3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of
Diabetes & Metabolic Disorders, 3(2),1-3.
4. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack
or Stroke Based on 3 Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC
Cardiology, 5(12), 1-9.
The author received an honorable PhD in mathematics and majored in engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is “math-physics and quantitative medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.
E-mail: g.hsu@eclariemd.com