Artificial Intelligence-Based Triage for Patients with Acute Abdominal Pain in emergency Department; a Diagnostic Accuracy Study
Introduction: Artificial intelligence (AI) is the development of computer systems which are capable of doing human intelligence tasks such as decision making and problem solving. AI-based tools have been used for predicting various factors in medicine including risk stratification, diagnosis and choice of treatment. AI can also be of considerable help in emergency departments, especially patients’ triage. Objective: This study was undertaken to evaluate the application of AI in patients presenting with acute abdominal pain to estimate emergency severity index version 4 (ESI-4) score without the estimate of the required resources. Methods: A mixed-model approach was used for predicting the ESI-4 score. Seventy percent of the patient cases were used for training the models and the remaining 30% for testing the accuracy of the models. During the training phase, patients were randomly selected and were given to systems for analysis. The output, which was the level of triage, was compared with the gold standard (emergency medicine physician). During the test phase of the study, another group of randomly selected patients were evaluated by the systems and the results were then compared with the gold standard. Results: Totally, 215 patients who were triaged by the emergency medicine specialist were enrolled in the study. Triage Levels 1 and 5 were omitted due to low number of cases. In triage Level 2, all systems showed fair level of prediction with Neural Network being the highest. In Level 3, all systems again showed fair level of prediction. However, in triage Level 4, decision tree was the only system with fair prediction. Conclusion: The application of AI in triage of patients with acute abdominal pain resulted in a model with acceptable level of accuracy. The model works with optimized number of input variables for quick assessment.
2. Goletsis Y, Papaloukas C, Fotiadis D, Likas A, Michalis L. Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis. IEEE Trans Biomed Eng. 2004;51(10):1717-25.
3. Mohktar MS, Redmond SJ, Antoniades NC, Rochford PD, Pretto JJ, Basilakis J, et al. Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data. Artif Intell Med. 2015;63(1):51-9.
4. Houthooft R, Ruyssinck J, van der Herten J, Stijven S, Couckuyt I, Gadeyne B, et al. Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med. 2015;63(3):191-207.
5. Kuo R, Huang M, Cheng W, Lin C, Wu Y. Application of a two-stage fuzzy neural network to a prostate cancer prognosis system. Artif Intell Med. 2015;63(2):119-33.
6. Liu N, Holcomb J, Wade C, Darrah M, Salinas J. Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patients. Shock. 2014;42(2):108-14.
7. Durani Y, Brecher D, Walmsley D, Attia MW, Loiselle JM. The Emergency Severity Index version 4: reliability in pediatric patients. Pediatr Emerg Care. 2009;25(11):751-3.
8. Fernandes C, Tanabe P, Gilboy N, Johnson L, McNair R, Rosenau A, et al. Five-level triage: a report from the ACEP/ENA Five-level Triage Task Force. J Emerg Nurs. 2005;31(1):39-50.
9. Christ M, Grossmann F, Winter D, Bingisser R, Platz E. Modern triage in the emergency department. Dtsch Arztebl Int. 2010;107(50):892-8.
10. Wuerz RC, Milne LW, Eitel DR, Travers D, Gilboy N. Reliability and validity of a new five‐level triage instrument. Acad Emerg Med. 2000;7(3):236-42.
11. Yurkova I, Wolf L. Under-triage as a significant factor affecting transfer time between the emergency department and the intensive care unit. J Emerg Nurs. 2011;37(5):491-6.
12. Gilboy N, Tanabe P, Travers DA. The Emergency Severity Index Version 4: changes to ESI level 1 and pediatric fever criteria. J Emerg Nurs. 2005;31(4):357-62.
13. Yoon P, Steiner I, Reinhardt G. Analysis of factors influencing length of stay in the emergency department. Cjem. 2003;5(3):155-61.
14. Fatovich D, Hirsch R. Entry overload, emergency department overcrowding, and ambulance bypass. Emerg Med J. 2003;20(5):406-9.
15. Shelton R. The emergency severity index 5-level triage system. Dimens Crit Care Nurs. 2009;28(1):9-12.
16. Abdoos M, Seyed Hosseini Davarani H, Hosseini Nejad H. Impact of Training on Performance of Triage: A Comparative Study in Tehran Emergency Department. Int J Hos Res. 2016;5(4):122-5.
17. Hossein-Nejad H, Banaie M, Seyedhosseini-Davarani S, Khazaeipour Z. Evaluation of the Significance of Vital Signs in the Up-Triage of Patients Visiting Emergency Department from Emergency Severity Index Level 3 to 2. Acta Med Iran. 2016;54(6):366-9.
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