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Rapid response to large-scale emergencies: a neural network model and a decision-tree algorithm




Sánchez Losada JA, Romero Sánchez S, Caamiña García M, Habed Lobos N, Jiménez Carrascosa JF, Touza Garma B, Gil González AM, Sanz Mata P



MĂ©dico de Emergencias, Servicio de Salud de Castilla-La Mancha (SESCAM), Toledo, Spain. TĂ©cnico de Sistemas y Comunicaciones SESCAM, Toledo, Spain.



Objective: The greatest challenge to decision-making during the management of
emergencies with multiple victims is uncertainty in an initially chaotic environment. Our
objective was to develop a predictive model to improve response and early resource
management in the early-phase environment of chaotic uncertainty during large-scale
emergencies.
Methods: A database of information on real incidents with multiple victims in Castile-La
Mancha, Spain, in the last 5 years was used to study the weight of 10 categorical variables
and their effect on the seriousness of the emergencies. A neural network was designed to
learn about these real cases, and a decision tree was generated, to study which of the 2
approaches gave the best results. An important design limitation was that nearly all the
incidents analyzed involved traffic accidents.
Results: The model based on decision-tree analysis gave more information and greater
variability. It proved superior to the neural network, identifying 6 homogeneous groups
according to the following factors: number of initial victims, type of incident, and
environment (P<.05).
Conclusions: A predictive model can be based on the considered variables in the interest
of improving resource management during a large-scale emergency. However,
development based on a larger number of real incidents of different types would be
needed before such a model could be applied during real future incidents.


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