Antibiotics 2019: Methodology of math-physical medicine (GH- | 45149
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International Research Journal of Pharmacy and Pharmacology
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Antibiotics 2019: Methodology of math-physical medicine (GH-Method)-Gerald C. Hsu-eclaireMD Foundation
Gerald C. Hsu
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.
Math-physical medicine starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.
Methodology of MPM on Diabetes 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 both wave 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 1,200 glucose waveforms with 21,000 data and then re-integrated them into 3 distinctive PPG waveform types which revealed different personality traits and psychological behaviors of type 2 diabetes patients between two variables, he used spatial analysis. 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 (self-learning, correction, and simplification), and artificial intelligence to achieve very high accuracy (95% to 99%) mg/dL and A1C is 6.5%. Since his health condition is stable, he no longer suffers from repetitive cardiovascular episodes.
This paper presents a system to more accurate prediction of glucose if possible, and measure of A1C glycohemoglobin, to achieve better control of the disease processes of T2D, and to predict untoward cardiovascular events. The author describes his math-physical medicine approach (MPM) to reach more accurate glucose predictions and A1C readings, utilizing mathematics, physics, engineering modeling, and computer science tools, instead of the current biochemical medicine approach (BCM) that mainly utilizes biology and chemistry. The attached Table 1 illustrates some fundamental differences between the traditional bio-chemical medicine (BCM) methodology and the non-traditional math-physical medicine (MPM) methodology.
Physicians are continually searching for approaches to urge individuals to receive a more advantageous way of life by practicing more or potentially diminishing caloric admission However, they are undermined by the precision of testing strategies. Patients with diabetes depend on blood glucose (BG) observing gadgets to deal with their condition. As some self-checking gadgets are turning out to be increasingly exact, it gets basic to comprehend the connection between framework exactness and clinical results, and the expected advantages of scientific precision. In one investigation of reproduced meter models got from the distributed attributes of 43 business meters analyst separated the distinctions in clinical execution that are legitimately connected with the meter qualities. The specialists detailed that a meter's deliberate inclination has a critical and reverse impact on HbA1c ( P <.01), while likewise influencing the quantity of extreme hypoglycemia occasions. Then again, mistake, characterized as the division of estimations past 5% of the genuine worth, is an indicator of serious hypoglycemia occasions ( P < .01). Both predisposition and mistake effectsly affect absolute every day insulin (TDI) and the quantity of fundamental glucose estimations every day ( P < .01). Besides, these connections can be precisely demonstrated utilizing straight relapse on meter predisposition and mistake. Two parts of meter precision, inclination and mistake plainly influence clinical results. While blunder has little impact on HbA1c, it will in general increment scenes of serious hypoglycemia. Meter predisposition effectsly affects every thought to be metric: a positive foundational inclination will decrease HbA1c, yet increment the quantity of extreme hypoglycemia assaults, TDI use, and number of finger.
More importantly, in the author’s opinion, his non-traditional research methodology of MPM can provide a quantitative proof with very high accuracy on other disease research work as well. After all, medicine is based on biology and chemistry while biology, chemistry, and engineering are based on physics. Mathematics is the mother of all sciences; even physics is based on mathematics. When we dig into our application problems down to foundation level, we are bound to be able to find out more facts and truth. This is what “math-physical medicine” is about.