Artificial Intelligence-Based Triage for Patients with Acute Abdominal Pain in emergency Department; a Diagnostic Accuracy Study
Journal Title: Advanced Journal of Emergency Medicine - Year 2017, Vol 1, Issue 1
Abstract
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.
Authors and Affiliations
Shervin Farahmand, Omid Shabestari, Meghdad Pakrah, Hooman Hossein-Nejad, Mona Arbab, Shahram Bagheri-Hariri
Keywords
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- EP ID EP378161
- DOI 10.22114/ajem.v1i1.11
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