Authors:
Omar Sadeq Salman, Nurul Mu’azzah Abdul Latiff, Omar H Salman, Sharifah Hafizah Syed Ariffin
Publication date: 2024/6
Journal: Neural Computing and Applications
Volume: 36
Issue: 17
Pages: 10109-10122
Publisher: Springer London
Description:
In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation poses challenges in collecting, organizing, processing, and making decisions about patients’ emergency levels. Existing methods for classifying (triaging) patients in such environments often yield inaccurate triage levels, necessitating a computational approach to enhance accuracy. This research aims to handle data from multiple heterogeneous sources in IoMT-based real-time telemedicine systems, analyze the data to accurately triage patients with the most urgent cases, and provide swift healthcare services. The proposed solution, the Data Processing with Triaging Model (DPTM), employs a hybrid approach that combines principal component analysis and …
Total citations: Cited by 7
Scholar articles:
A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems
OS Salman, NM Abdul Latiff, OH Salman… – Neural Computing and Applications, 2024