Authors:
Varun Kumar Nomula

Addresses:
1Department of Analytics/Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America. vnomula3@gatech.edu1 

Abstract:

This research paper introduces an innovative method for analyzing medical sensor data through the integration of physiological models. The core of this approach lies in combining established mathematical models of human physiology with cutting-edge data analytics techniques, creating a powerful tool for interpreting complex medical data. This methodology allows for a more comprehensive understanding of patient health and various medical conditions, leveraging the precision of physiological models with the vast array of data available from medical sensors. The paper meticulously outlines the development of this novel approach, detailing how physiological models are intricately woven into the fabric of data analysis. By doing so, it aims to significantly enhance the accuracy and reliability of medical diagnostics and monitoring. This is particularly critical in the context of healthcare, where precise and reliable data interpretation can have profound implications on patient care and treatment outcomes. The paper explores the application of this methodology across diverse medical sensor datasets. Through rigorous experimentation and analysis, it showcases how this approach can be effectively applied in different scenarios, highlighting its versatility and adaptability in various medical contexts. The results obtained from these experiments are also extensively discussed. These findings underscore the potential of this innovative approach to revolutionize the field of medical data analysis. By providing deeper and more accurate insights into patient health, it stands to improve clinical decision-making processes significantly. The paper concludes by emphasizing the value of this approach for healthcare professionals, offering them a more nuanced and comprehensive tool for interpreting medical sensor data. This, in turn, could lead to more informed healthcare strategies and better patient outcomes.

Keywords: Medical Sensor Data; Physiological Models; Data Analytics; Healthcare Monitoring; Decision-Making Processes; Effective Management; Patients' Conditions; Physio Data Integration Process.

Received on: 12/06/2023, Revised on: 10/09/2023, Accepted on: 03/11/2023, Published on: 15/12/2023

FMDB Transactions on Sustainable Health Science Letters, 2023 Vol. 1 No. 4, Pages: 186-197

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