利用年度葡萄糖和代谢指数分段数据分析患心血管疾病、中风或肾脏并发症的风险概率(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代谢数学模型,该模型包含10个类别,包括4个输出类别(体重、血糖、血压以及其他实验室检测数据包括血脂和ACR)和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),它具有上述代谢类别和元素的综合得分。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, Stroke, or Renal Complications 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, other labtested data including lipids & ACR) and six input categories (food, water drinking, exercise, sleep, stress, routine life patterns and safety measures). These 10 metabolic 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 metabolic categories and elements. Since 2012, he has collected and stored ~2 million data from his own body health conditions and personal lifestyle details.
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