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

Kadum, Sara Yahya; Salman, Omar Hussein; Taha, Zahraa K.; Said, Amal Bati; Ali, Musab A. M.; Qassim, Qais Saif; Aal-Nouman, Mohammed I.; Mohammed, Duraid Y.; baker, Baraa M. Al; Abdalkareem, Zahraa Adnan

doi:10.1007/s13721-022-00407-w

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…The work [22] proposed the Remote Triage in Telemedicine (ML-ART), intended as a framework for telemedicine within the IoMT environment. The work aimed to improve remote triage for patients with multichronic diseases such as heart disease, hypertension, and diabetes within the telemedicine system.…”

Section: Related Workmentioning

confidence: 99%

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Omar Sadeq Salman,

Abdul Latiff,

Syed Arifin

et al. 2024

Elektrika

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Current environmental conditions and human lifestyles have resulted in the emergence of numerous diseases. The medical field generates an enormous amount of new data each year for remote monitoring of patients. Due to increased data growth in the medical and healthcare industries, accurate medical data analysis has been advantageous to early patient care. However, physicians often face challenges in accurately diagnosing diseases in patients far from hospitals. Therefore, utilizing remote patient systems (telemedicine systems) due to the complexities associated with their chronic conditions. On the other hand, predicting illness is also a challenging task. Thus, data extracted from heterogeneous, fast-flowing, and reliable sources is crucial for decision-making and disease prediction. This research paper aims to utilize supervised Machine Learning (ML) techniques to predict chronic diseases such as heart and hypertension based on the patient’s features or symptoms by analyzing patient data collected by sensors and sources enabled by the Internet of Medical Things (IoMT). Supervised ML technology in Hadoop and Spark environments is employed to guarantee that this classification accurately identifies individuals with chronic illnesses. The methods are evaluated using 55,680 patient records to discover the proper match between the data set and the final disease-predicted result. The results demonstrate that the proposed procedure employing the Decision Tree (DT) algorithm is 94% accurate, and DT outperforms the other four ML algorithms. This includes the Support Vector Machine (SVM), a Naive Bayes (NB) model, Random Forest (RF), and Logistic Regression (LR) in terms of both performance and accuracy metrics (precision, recall, and F-score).

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Section: Related Workmentioning

confidence: 99%

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Omar Sadeq Salman,

Abdul Latiff,

Syed Arifin

et al. 2024

Elektrika

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“…One of the most widely used AI frameworks in emergency management is machine learning, which involves the use of algorithms to automatically learn patterns in data and make predictions or decisions based on those patterns. Machine learning can be applied in a variety of emergency management scenarios, including predicting the severity of natural disasters, identifying potential terrorist threats, accident detection [98], panic detection [23], evacuation route planning [60], detecting the spread of infectious diseases, and prioritising the patients in case of emergency [99]. Neural networks can be used to classify emergency situations based on their severity and urgency, or to identify the most effective response strategies based on past emergency response data, or for localization [100].…”

Section: Artificial Intelligence Framework and Modelsmentioning

confidence: 99%

“…The Medical Emergency scenario in The Internet of Emergency Services (IoES) involves the use of advanced technologies to improve the emergency medical response [77,99,119,142,143]. In this scenario, the IoES leverages the Internet of Things (IoT) and other technologies to collect and analyze data in real-time, provide timely and accurate medical assistance to patients, and enhance the overall emergency medical response.…”

Section: Medical Emergencymentioning

confidence: 99%

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

Damaševičius

Bačanin

Misra

2023

JSAN

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The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

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“…Several AI-based solutions have been tested to assist triage nurses and standardize the triage process, having promising outcomes for the improvement of triage flow and patient outcomes [1]. For example, ML-based remote triage (ART) uses patient data and transfers it through a gateway to telemedicine servers within the hospital and is then utilized to triage patients into categories dependent on the emergency [14]. The digitalization of EDs and the capacity of current-generation computers allow for algorithm-based data evaluation and risk stratification for specific clinical endpoints beyond the triage level, providing a bright outlook on the implementation of AI in EDs [15].…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review

Tyler,

Olis,

Aust

et al. 2024

Cureus

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The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.

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

Cited by 10 publications

References 25 publications

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review

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