From a Public Health Point of View to Investigate the Control of Obesity, Diabetes, and Cardiovascular Risk Via Nutrition and Exercise (GH-Method: Math-Physical Medicine)

Gerald C. Hsu
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Abstract

Methods The author spent 23,000 hours during the past 8.5 years using math-physical medicine to conduct his research. He has collected and processed ~1.5 million data, including ~300,000 medical conditions, and ~1.2 million lifestyle details. He then utilized the GH-Method: math-physical medicine (MPM) which involves advanced mathematics, optical physics, signal processing, energy and wave theories, statistics, big data analytics, machine learning, artificial intelligence to develop five prediction models, including weight, FPG, PPG, adjusted glucose, and HbA1C.
从公共卫生角度探讨营养和运动对肥胖、糖尿病和心血管风险的控制(GH-Method:数学-物理医学)
方法采用数理医学方法,历时8.5年,历时23000小时进行研究。他收集并处理了150万条数据,其中包括30万条医疗条件和120万条生活方式细节。利用高等数学、光学物理、信号处理、能量与波动理论、统计学、大数据分析、机器学习、人工智能等GH-Method:数学-物理医学(MPM)方法,建立了体重、FPG、PPG、调整血糖、糖化血红蛋白等5个预测模型。
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