This paper discusses the author’s biomedical research work based on the GH-Method: math-physical medicine (MPM) approach over the past decade. This is significantly different from the traditional medical research using biochemical approach and simple statistical methods. He uses his own type 2 diabetes (T2D) metabolic conditions as a case study including several application examples as illustrations and explanations of the MPM methodology. The MPM methodology will be described, then followed by 10 application examples to show how he applied his knowledge and disciplines in mathematics, physics, engineering modeling, computer science tools, and psychology during his 10-years of biomedical research, especially in the domain of lifestyle, metabolism, chronic diseases, diabetes, cardiovascular diseases, and renal complications. The following list highlights the math-physical concepts, theories, principles, or equations used in the 10 application examples: 1. Topology, finite element method 2. Time-domain analysis, correlation and regression model, pattern recognition, segmentation analysis 3. Signal processing, trial and error method, regression analysis 4. Artificial intelligence (AI) auto-correction, quantum mechanics, safety margin of engineering design 5. Optical physics, AI, perturbation theory of quantum mechanics 6. Wave theory, Fourier transform, frequency-domain analysis 7. Structural engineering modeling, solid mechanics (both elastic and plastic), fluids dynamics, energy theory 8. Pattern recognition, behavior psychology 9. Spatial analysis, time-series analysis 10. Big data analytics, AI, software engineering Using MPM, a non-traditional medical research methodology, provides many quantitative proofs with a high degree of accuracy (higher precision) compared to other disease research results. Medicine is based on biology and chemistry, while biology, chemistry, and engineering are based on physics, and physics is based on mathematics. Logically, mathematics is the mother of all sciences. When we explore our application problems down to the foundation level, we can discover more facts and deeper truths. This is the logical essence of “math-physical medicine.” Using this MPM model, the accuracy of medical evaluations, along with the precision of predictive models can be greatly improved, with dramatic benefits to the patients.
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