An Intelligent System for Prioritising Emergency Services Provided for People injured in Road Traffic Accidents
| Autor: | Sharareh R. Niakan Kalhori, Mohammad Taghi Taghavifard, Pegah Farazmand, Khatereh Farazmand |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Vital signs
Poison control 02 engineering and technology Computer security computer.software_genre Occupational safety and health lcsh:Social Sciences 03 medical and health sciences 0302 clinical medicine Injury prevention 0202 electrical engineering electronic engineering information engineering Medicine Adaptive neuro fuzzy inference system business.industry General Arts and Humanities General Social Sciences Human factors and ergonomics 030208 emergency & critical care medicine Emergency department medicine.disease Triage lcsh:H 020201 artificial intelligence & image processing Medical emergency business General Economics Econometrics and Finance computer |
| Zdroj: | Mediterranean Journal of Social Sciences, Vol 7, Iss 1 S1 (2016) |
| ISSN: | 2039-2117 2039-9340 |
| Popis: | Excessive road traffic accidents are the cause of referrals of a large number of injured people to hospitals. However, shortage of resources does not allow caring for all of them at the same time. Therefore, injured individuals should be prioritised by a triage unit. Patients with serious life-threatening conditions should be sent as the first priority to the emergency department to receive required care. This paper aims to design a triage model for categorising injured individuals using two different methods: Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The models were built with a data set of 3015 data designed by Iranian medical experts and were based on patients` general appearance , vital signs and chief complaints. When a patient presents to the triage unit, the system analyses the data given and patient`s emergency status can be reported straightaway. This reduces the triage time and the queue of patients at the emergency department. Both models were tested by 3 groups of data with a total number of 417 data. Reliability and validity were assessed. Results showed that overall ANFIS model performed better in categorising patients. DOI: 10.5901/mjss.2016.v7n1s1p354 |
| Databáze: | OpenAIRE |
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