Gerald C. Hsu
eclaireMD Foundation, USA
Introduction: The author has spent 8 years collecting and processing ~ 1.5 million data and researching medical conditions and lifestyle details on a patient (himself) with three chronic diseases, such as hyperlipidemia, type 2 diabetes (T2D), and hypertension. He suffered five cardiac episodes from 1994 through 2006. This clinic report focuses on his risk probability of having a heart attack or stroke due to his overall metabolic conditions based on medical records from the year 2000 and lifestyle management details collected since 2012. The author, who has no formal medical training, is a research scientist in the field of diabetes and metabolic disorders. His assessment emphasizes on the direct inter-relationship between metabolic syndrome and heart disease or stroke.
Method: Initially, the author established a “static baseline condition” based on the patient’s age, gender, race, family history, unhealthy habits, and waistline. Based on his readings from different medical publications, he has made two following assumptions: First, by using fluid dynamics concept, the author hypothesized that the major causes of blood flow blockage are due to high glucose and high cholesterol. Second, by using solid mechanics concept, the author speculated that the major causes of artery rupture are due to high glucose and high blood pressure.
Results: The author can discuss many detailed findings from his analyses; however, he will focus on risk probability of heart attack or stroke in this article. Although the data are slightly different for three different analyses numerically, these three trends of risk reduction when time progresses forward is identical, i.e. his risk probability of having a heart attack or stroke is reduced year after year. The calculated significant risk probabilities for this patient are shown in Significant Risk Probability of Heart Attack & Stroke.
(1) 74% in 2000 (followed by three cardiac episodes during 2001 - 2006);
(2) 69% in 2012 decreased to 26.4% in 2017;
(3) The risk probability in 2017 is 26.4% using his mathematical dynamic models, which is compatible with 26.7% by Framingham Study;
(4) Sensitivity range from all of those WF (weighting factor) variance analyses: +/- 10% to +/- 18%.
Conclusion: The calculated risk probability results have been validated by the patient’s health examination reports from hospitals over a long period from 2000 through 2017. From this study of big data dynamic simulation approach using math-physical medicine, it can provide the author, who has chronic diseases, an early warning of having another heart attack or stroke in the future.
Gerald C. Hsu has completed his 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.