A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries.
In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predi...
| Publicado en: | International Journal of Injury Control & Safety Promotion Vol. 28; no. 2; pp. 222 - 233 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150512612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150512612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17457300 1F4U jtl: International Journal of Injury Control & Safety Promotion issn: 17457300 maglogo: Y pubinfo: dt: Jun2021 vid: 28 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 150512612 149642039 150512612 NLM33818273 10.1080/17457300.2021.1907596 NLM33818273 150512612 ppf: 222 ppct: 11 formats: tig: atl: A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries. aug: au: Taamneh, Salah Taamneh, Madhar M. affil: Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa, Jordan sug: subj: Accidents, Traffic Wounds and Injuries Epidemiology Emergency Service Pharmacokinetics ab: In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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