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Journal of Nutrition and Human Health | Volume 3
May 23-24, 2019 | Vienna, Austria
Joint Event
2
nd
International Conference on
Gastroenterology and Digestive Disor
ders
17
th
International Conference on
Nutrition and Fitness
&
Methodology of math-physical medicine
Gerald C Hsu
eclaireMD Foundation, USA
M
ath-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%).
Speaker Biography
Gerald C Hsu received an honourable 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 modelling, signal processing,
computer science, big data analytics, statistics, machine learning, and AI.
His focus is on preventive medicine using prediction tools. He believes
that the better the prediction, the more control you have.
e:
g.hsu@eclairemd.com