利用年度葡萄糖和代谢指数分段数据分析心血管疾病或中风的风险概率(GH方法:数学-物理医学)

Gerald C. Hsu
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引用次数: 0

摘要

方法2014年,作者运用拓扑概念、有限元工程技术和非线性代数运算建立了一个包含10个类别的代谢数学模型,其中包括4个输出类别(体重、血糖、血压和其他实验室测试数据,包括血脂和ACR), 6个输入类别(食物、饮水、运动、睡眠、压力、日常生活方式和安全措施)。这10个代谢类别包括大约500个详细的元素。他进一步定义了一个新的参数,称为代谢指数(MI),它具有上述10种代谢类别500个元素的综合得分。从2012年开始,他收集并存储了约200万条关于自己身体健康状况和个人生活方式细节的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk Probability of Having a Cardiovascular Disease or Stroke Using Annual Segmented Data of Glucose and Metabolism Index (GH Method: Math-Physical Medicine)
Method In 2014, the author applied topology concept, finite-element engineering technique, and nonlinear algebra operations to develop a mathematical metabolism model which contains ten categories including four output categories (weight, glucose, BP, and other lab-tested data including lipid & ACR), and six input categories (food, water drinking, exercise, sleep, stress, routine life patterns and safety measures). These 10 metabolism categories include approximately 500 detailed elements. He further defined a new parameter referred to as the metabolism index (MI) that has a combined score of the above 10 metabolism categories with 500 elements. Since 2012, he has collected and stored ~2 million data of his own body health conditions and personal lifestyle details.
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