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      Applications of Machine Learning Approaches in Emergency Medicine; a Review Article

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          Abstract

          Using artificial intelligence and machine learning techniques in different medical fields, especially emergency medicine is rapidly growing. In this paper, studies conducted in the recent years on using artificial intelligence in emergency medicine have been collected and assessed. These studies belonged to three categories: prediction and detection of disease; prediction of need for admission, discharge and also mortality; and machine learning based triage systems. In each of these categories, the most important studies have been chosen and accuracy and results of the algorithms have been briefly evaluated by mentioning machine learning techniques and used datasets.

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          Most cited references34

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          The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model.

          To develop an acute kidney injury risk prediction model using electronic health record data for longitudinal use in hospitalized patients.
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            Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index

            Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation.
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              Predicting hospital admission at emergency department triage using machine learning

              Objective To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. Methods This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. Results A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91). Conclusion Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
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                Author and article information

                Journal
                Arch Acad Emerg Med
                Arch Acad Emerg Med
                AAEM
                Archives of Academic Emergency Medicine
                Shahid Beheshti University of Medical Sciences (Tehran, Iran )
                2645-4904
                2019
                3 June 2019
                : 7
                : 1
                : 34
                Affiliations
                [1 ]Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
                Author notes
                Corresponding author: Hamed Malek; Shahid Beheshti University, Shahid Shahriari Square, Daneshjou Boulevard, Shahid Chamran Highway, Tehran, Iran. Email: h_malek@sbu.ac.ir, Phone/Fax: +98 (21) 29904106
                Article
                aaem-7-e34
                6732202
                31555764
                a051e352-aadd-414c-a36e-ee9238eb75de

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License, ( http://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : May 2019
                : June 2019
                Categories
                Review Article

                artificial intelligence,machine learning,emergency medicine,emergency service,hospital,triage

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