Using Four Clinical Cases to Examine the Accuracy of Predicted Postprandial Plasma Glucose Via AI Glucometer Tool (GH-Method: Math-Physical Medicine)

Author Details

Gerald C. Hsu, Than Than Aye, Kyaw Sear Thet

Journal Details

Archives of Cardiology and Cardiovascular Diseases - ISSN - 2638-4744, Volume 3, Issue 1

Published

Published: 7 May 2020 | Article Type :

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doi 10.22259/2638-4744.0301005

Abstract

The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI) to derive the mathematical metabolism model. In this study, he utilized his MPM approach to investigate four clinical cases to examine the accuracy of the predicted postprandial plasma glucose via artificial intelligence glucometer tool.

Keywords: Type 2 diabetes, metabolism, metabolic conditions, lifestyle data, artificial intelligence, AI Glucometer tool, and math-physical medicine.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Volume 3, Issue 1

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Citation:

Gerald C. Hsu, Than Than Aye, Kyaw Sear Thet. (2020-05-07). "Using Four Clinical Cases to Examine the Accuracy of Predicted Postprandial Plasma Glucose Via AI Glucometer Tool (GH-Method: Math-Physical Medicine)." *Volume 3*, 1, 17-21