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

Salman, Omar Hussein; Taha, Zahraa K.; Alsabah, Muntadher; Hussein, Yaseein Soubhi; Mohammed, Ahmed; Aal-Nouman, Mohammed I.

doi:10.1016/j.cmpb.2021.106357

Cited by 40 publications

(14 citation statements)

References 96 publications

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“…Machine learning techniques have been widely applied in varied medical fields in prioritizing patients for specific fast healthcare services, such as triage, disease detection, prediction, and classification [ 31 ]. To our knowledge, this is the first study to design a decision support tool for predicting the need for ECG acquisition, using machine learning techniques to analyze ED triage data.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

Tsai

Chiang

et al. 2022

JPM

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The machine learning-assisted electrocardiogram (ECG) is increasingly recognized for its unprecedented capabilities in diagnosing and predicting cardiovascular diseases. Identifying the need for ECG examination early in emergency department (ED) triage is key to timely artificial intelligence-assisted analysis. We used machine learning to develop and validate a clinical decision support tool to predict ED triage patients’ need for ECG. Data from 301,658 ED visits from August 2017 to November 2020 in a tertiary hospital were divided into a development cohort, validation cohort, and two test cohorts that included admissions before and during the COVID-19 pandemic. Models were developed using logistic regression, decision tree, random forest, and XGBoost methods. Their areas under the receiver operating characteristic curves (AUCs), positive predictive values (PPVs), and negative predictive values (NPVs) were compared and validated. In the validation cohort, the AUCs were 0.887 for the XGBoost model, 0.885 for the logistic regression model, 0.878 for the random forest model, and 0.845 for the decision tree model. The XGBoost model was selected for subsequent application. In test cohort 1, the AUC was 0.891, with sensitivity of 0.812, specificity of 0.814, PPV of 0.708 and NPV of 0.886. In test cohort 2, the AUC was 0.885, with sensitivity of 0.816, specificity of 0.812, PPV of 0.659, and NPV of 0.908. In the cumulative incidence analysis, patients not receiving an ECG yet positively predicted by the model had significantly higher probability of receiving the examination within 48 h compared with those negatively predicted by the model. A machine learning model based on triage datasets was developed to predict ECG acquisition with high accuracy. The ECG recommendation can effectively predict whether patients presenting at ED triage will require an ECG, prompting subsequent analysis and decision-making in the ED.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

Tsai

Chiang

et al. 2022

JPM

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“…Considering the current COVID-19 pandemic situations and the foreseen post-pandemic era, it is highly expected that several advanced remote health care services and challenges will emerge [291]. The role of AI will be immense in the next generation remote health care systems for automating different processes, e.g., automatic monitoring, diagnosis and corresponding recommendation for the treatments [292]. Furthermore, the integration of various enabling technologies into the remote health care systems would likely to make communication networks much more complex, and hence future research is required in this direction as well.…”

Section: A Remote Monitoring and Aidmentioning

confidence: 99%

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Ahmad¹,

Asghar²,

Kumar³

et al. 2022

Preprint

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This article surveys the latest developments in the technological landscape that enables remote health care. First, the most important technologies that are crucial for remote health care such as 5G, medical IoT, big data analytics, machine learning, and distributed ledger technologies, etc., are outlined, and then a thorough survey on those technologies with latest state-of-the-art applications in remote health care is conducted. Furthermore, very important research challenges and future research directions are laid out to initiate further research on those very important technologies that enable remote health care.

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“…Part of the solution is expected to involve the integration of technological advancements into routine healthcare [13] , especially from the field of artificial intelligence (AI). One patient-oriented approach gaining popularity is the use of intelligent digital self-assessment tools, known as symptom checkers [14] , [15] .…”

Section: Introductionmentioning

confidence: 99%

“…These symptom checkers are able to gather and summarize medical information, allocate patients to an appropriate level of care, and suggest potential diagnoses and treatment options. As such, they carry the potential to save trained practitioners time, decrease the overuse of medical services, and minimize unnecessary mistakes [13] , [19] , [20] , [21] , [22] . All of which, theoretically, could reduce the load on healthcare systems and improve healthcare quality.…”

Section: Introductionmentioning

confidence: 99%

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study

Ben-Shabat

Sharvit

Meimis

et al. 2022

International Journal of Medical Informatics

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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

Cited by 40 publications

References 96 publications

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study

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