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Patient classification algorithm at urgency care area of a hospital based on the triage system

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Języki publikacji
EN
Abstrakty
EN
The time passed in the urgency zone of a hospital is really important, and the quick evaluation and selection of the patients who arrive to this area is essential to avoid waste of time and help the patients in a higher emergency level. The triage, an evaluation and classification structured system, allows to manage the urgency level of the patient; it is based on the vital signs measures and clinical data of the patient. The goal is making the classification in the shortest possible time and with a minimal error percentage. Levels are allocated according to the concept that what is urgent is not always serious and that what is serious is not always urgent. In this work, we present a computational algorithm that evaluates the patients within the fever symptomatic category, we use fuzzy logic and decision trees to collect and analyze simultaneously the vital signs and the clinical data of the patient through a graphical interface; so that the classification can be more intuitive and faster. Fuzzy logic allows us to process data and take a decision based on incomplete information or uncertain values, decision trees are structures or rules sets that classify the data when we have several variables.
Rocznik
Tom
Strony
87--94
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Bibliografia
  • [1] ADLER C. et al., IT-Supported Management of Mass Casualty Incidents: The e-Triage Project, Proceedings of the 8th International ISCRAM Conference Lisbon, Portugal, May 2011.
  • [2] GAO T. et al., The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage. IEEE Transactions on biomedical circuits and systems, September 2007, Vol. 1, No. 3.
  • [3] GILBOY N. et al., Emergency Severity Index, a triage tool for emergency department care. Implementation Handbook. AHRQ Publications, 2012.
  • [4] GOMEZ J. et al., Sistema de triaje espaol (SET). Sociedad Espaola de Medicina de Urgencias (SEMES), Andorra, 2003.
  • [5] GOMEZ J. et al., Clinical validation of the new version of the Triage Assistance Program (web e-PAT v3) of the Andorran Triage Model (MAT) and Spanish Triage System (SET). Reliability, usefulness and validity in the pediatric and adult population. Servei d’Urgncies, Hospital Nostra Senyora de Meritxell, 2005.
  • [6] ROKACK L., MAIMON O., Data mining and knowledge discovery handbook, second edition, Springer Science+Business Media, 2010, pp. 149-166.
  • [7] ROKACK L., MAIMON O., Data mining with Decision trees: Theory and Applications, Series in Machine Perception and Artificial Intelligence, 2008, Vol. 69.
  • [8] ROSS T., Fuzzy Logic with Engineering Applications, John Wiley and Sons, Ltd. 2004.
  • [9] SOLER W., Gomez M., Triaje: a key tool in emergency care. An. Sist. Sanit. Navar, 2010, No. 33 Supl. 1, pp. 55-68.
  • [10] SANCHEZ R. et al., El triaje en urgencias en los hospitales espaoles. U. de urgencias, Hospital Nuestra Seora del Prado, Toledo Espaa, 2012.
  • [11] SAJFERT Z. et al., Application of fuzzy logic into process of decision making regarding selection of managers. African Journal of Business Management, March, 2012, Vol. 6 (9), pp. 3221-3233.
  • [12] SUMATHI S. SUREKA P., Computational Intelligence Paradigms: theory & applications using MATLAB, Taylor & Francis group, 2010, pp. 9-11, 203-207.
  • [13] TOLIFE, TRIUS Triage Station and Emerges Platform. www.tolife.com.br.
  • [14] WILLIAMS G., Data Mining with Rattle and R, Springer, 2011, pp. 205-244.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6c468b18-3d7d-452a-93d2-7f15a5e3d44f
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