Applying the Distributional Data Analysis Tool of Biomarker Density with the Collected Daily Data of 5 Biomarkers over 7.5 Years from a Patient with Chronic Diseases to Investigate his Overall Health Conditions Based on GH-Method: Math-Physical Medicine (No. 516)

Abstract

Gerald C Hsu

Recently, the author conducted a series of medical research projects by applying a distributional data density analysis tool on his weight, glucose, blood pressure (BP), and heart conditions, while using his collected big data regarding certain biomarker’s density distribution for the selected years. In this article, he consolidated five selected biomarkers, weight, finger piercing estimated average glucose (eAG), heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP), within a longer time span of 7.5 years (4/1/2014 - 9/13/2021). The reason he omitted his continuous glucose monitoring (CGM) sensor eAG, sensor fasting plasma glucose (FPG), and sensor postprandial plasma glucose (PPG) is due to their relative shorter data availability timeframe of 3.5 years (5/8/2018 - 9/13/2021). With the personal data, he can interpret the results and explore additional and information since he is most familiar with his own health conditions. Of course, these findings regarding his own body is also applicable to other patients with chronic diseases. The main purpose of writing this series of research articles is to further demonstrate the applicability and power of using this specific distributional data density analysis tool.

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