Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases

Area of Science:

• Biomedical Engineering
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Telemedicine and remote patient monitoring (RPM) systems have seen increased adoption since the COVID-19 pandemic.
• These systems leverage technologies like wireless sensors, cloud computing, and machine learning.
• Existing systems aim to enhance healthcare delivery efficiency and patient care.

Purpose of the Study:

• To propose a real-time remote patient monitoring system with an accessible, compact, accurate, and low-cost design.
• To establish an end-to-end communication interface between healthcare providers and patients for continuous care.
• To facilitate remote healthcare services for patients needing ongoing care or post-discharge monitoring without disrupting daily routines.

Main Methods:

• Development of a real-time remote patient monitoring system integrating various advanced technologies.
• Implementation of an end-to-end communication interface for patient-practitioner interaction.
• Evaluation of the system using 1177 records from the MIMIC-III clinical dataset.

Main Results:

• The proposed system achieved 88.7% accuracy in generating patient alerts using a logistic regression classification algorithm.
• The system demonstrated high computational speed with a processing time of less than 2 minutes.
• The system's performance indicates robustness and quality for real-time applications.

Conclusions:

• The developed remote patient monitoring system offers an efficient and accurate solution for continuous healthcare.
• The system's low-cost and accessible design makes it suitable for improving doctor-to-patient ratios and supporting remote/elderly care.
• This technology enhances healthcare accessibility by enabling remote monitoring without impacting patients' daily lives.