A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work

•Identifies the relationship between machine learning methods and telemedicine architecture based on a cross over matching.•Deliver a clear understanding on various aspects of E-triage and priority systems based on machine learning algorithms.•Presents sets of inputs data, features, variables, outcome results and targets for machine learning in telemedicine system.•Highlights the open research challenges that might prevent the utility of using machine learning in telemedicine systems.•Presents step by step machine learning workflow and data processing procedure in healthcare and telemedicine systems With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required. This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications. An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigat