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Volume 08
Clinical Pharmacology & Biopharmaceutics
ISSN: 2167-065X
Pharmacology 2019
World Heart Congress 2019
August 19-20, 2019
JOINT EVENT
conferenceseries
.com
August 19-20, 2019 Vienna, Austria
&
7
th
World Heart Congress
24
th
World Congress on
Pharmacology
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%).
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 Signal Processing Techniques to Predict PPG for T2D. International Journal of
Diabetes & Metabolic Disorders, 3(2),1-3.
3.
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.
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.
Biography
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. (Up to 100 words)
Gerald C. Hsu, Clin Pharmacol Biopharm, Volume 08