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Cancer Congress 2019 Journal of Cancer Immunology &Therapy | Volume 2
Page 13
July 22-23, 2019 | Brussels, Belgium
OF EXCELLENCE
IN INTERNATIONAL
MEETINGS
YEARS
CANCER SCIENCE AND THERAPY
2nd Global Congress on
GH METHOD: METHODOLOGY OF
MATH-PHYSICAL MEDICINE
Introduction: This paper describes the math-physical medicine approach
(MPM) of medical research utilizing mathematics, physics, engineering
models and computer science, instead of the current biochemical medicine
approach (BCM) that mainly utilizes biology and chemistry.
Methodology of MPM: 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. Further author
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. Each factor
has a different contribution margin to the glucose formation. He developed
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 behaviours of type 2 diabetes patients. For single time-
stamped variables, he used traditional time-series analysis. For interactions
between two variables, he used spatial analysis. Furthermore, he also applied
Gerald C Hsu, J Cancer Immunol Ther 2019, Volume 2
Gerald C Hsu has completed his PhD in Mathe-
matics and has been majored in Engineering at
MIT. He has 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 nutri-
tion during 2010-2013, then conducted research
during 2014-2018. His approach is math-physics
and quantitative medicine based on mathe-
matics, physics, engineering modelling; signal
processing, computer science, big data analyt-
ics, 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.
Gerald C Hsu
EclaireMD Foundation, USA
BIOGRAPHY