Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services

Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services

The remarkable increase in the number of patients poses challenges to healthcare providers in telemedicine systems, and certain challenges are associated with the difficulties of identifying the most urgent emergencies to save lives. Remote monitoring of patients with multiple chronic diseases (MCDs...

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Bibliographic Details
Published in:Network modeling and analysis in health informatics and bioinformatics (Wien) Vol. 12; no. 1; p. 11
Main Authors: Kadum, Sara Yahya, Salman, Omar Hussein, Taha, Zahraa K., Said, Amal Bati, Ali, Musab A. M., Qassim, Qais Saif, Aal-Nouman, Mohammed Imad, Mohammed, Duraid Y., Al baker, Baraa M., Abdalkareem, Zahraa A.
Format: Journal Article
Language:English
Published: Vienna Springer Vienna 03-01-2023
Springer Nature B.V
Subjects:
Algorithms
Applications of Graph Theory and Complex Networks
Artificial intelligence
Bioinformatics
Blood pressure
Chronic illnesses
Computational Biology/Bioinformatics
Computer Science
Datasets
Decision making
Decision trees
Diabetes
Diabetes mellitus
Disease
Emergency medical care
Emergency medical services
Health care
Health Informatics
Health services
Heart
Heart diseases
Hospitals
Hypertension
Hypotension
Internet of medical things
Learning algorithms
Literature reviews
Machine learning
Neural networks
Original Article
Patients
Performance enhancement
Remote monitoring
Support vector machines
Telemedicine
Triage
Artificial intelligent
Diabetes
Internet of medical things (IoMT)
Remote monitoring
Sensor
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Summary:The remarkable increase in the number of patients poses challenges to healthcare providers in telemedicine systems, and certain challenges are associated with the difficulties of identifying the most urgent emergencies to save lives. Remote monitoring of patients with multiple chronic diseases (MCDs) is a major issue that must be addressed. This work aims to improve the triage system for remote patients who are far from hospital and use telemedicine system by considering the variation in their chronic diseases (chronic heart, hypertension, hypotension, and diabetes diseases). The proposed framework named machine learning-based remote triage framework in telemedicine (ML-ART) collects the patient’s data using medical sensors and sources within Internet of medical things (IoMT) environment, transfers the data through gateway to telemedicine servers in the hospital, where machine learning is performed to classify (triages) each patient into one of five categories (normal, cold, sick, urgent, and risk) depending on the medical emergency level of the patients. The simulation results showed that the decision tree (DT) algorithm has the most accurate result, 100%, compared to the relevant algorithm (neural network (NN) 97%, support vector machine (SVM) 91%, and random forest (RF) 97%). The performance of (DT) logically matched the medical triage procedure. Moreover, the (ML-ART) outcomes improve the performance of e-triage system for remote patients and pave the way for future works in telemedicine systems through moving from remote monitoring to remote diagnostics.
ISSN:2192-6670
2192-6662
2192-6670
DOI:10.1007/s13721-022-00407-w