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Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems.

Abdulrahman Ahmed Jasim1, Oguz Ata2, Omar Hussein Salman3

  • 1Dept. of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey; Collage of Engineering, Al-Iraqia University, Baghdad, Iraq.

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Summary

The Real-time Triage Optimization Framework (RTOF) significantly improves emergency patient prioritization using Internet of Medical Things (IoMT) data. This AI-driven approach achieves 98% accuracy, outperforming current methods and reducing hospital congestion.

Keywords:
IoMTMultisource DataPatient TriageReal-Time Triage Optimisation Framework (RTOF)Telemedicine

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Telemedicine

Background:

  • The Internet of Medical Things (IoMT) enables remote patient monitoring but generates vast data, complicating emergency patient prioritization.
  • Existing triage methods struggle to effectively manage the influx of data from integrated healthcare devices.

Purpose of the Study:

  • To enhance emergency patient prioritization by implementing the Real-time Triage Optimization Framework (RTOF).
  • To leverage diverse IoMT data for improved real-time decision-making in emergency care.

Main Methods:

  • Utilized diverse IoMT data, including sensor readings and electronic medical records (EMR).
  • Applied five machine-learning algorithms to a dataset of 100,000 patients with hypertension and heart disease.
  • Developed the Real-time Triage Optimization Framework (RTOF) for data processing and analysis.

Main Results:

  • The RTOF achieved a 98% triage accuracy rate in a simulated telemedicine environment.
  • The Random Forest algorithm demonstrated superior performance with 98% accuracy, 99% precision, 98% sensitivity, and 100% specificity.
  • Significant improvements were observed compared to existing triage methodologies.

Conclusions:

  • RTOF surpasses current triage frameworks, enhancing telemedicine quality and efficacy.
  • The framework offers a scalable solution for hospital congestion and real-time resource allocation.
  • RTOF can mitigate overcrowding, expedite interventions, and support adaptable telemedicine networks.