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Statistical models for fever forecasting based on advanced body temperature monitoring

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Statistical models for fever forecasting based on advanced body temperature monitoring

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dc.contributor.author Jordán Núñez, Jorge es_ES
dc.contributor.author Miró Martínez, Pau es_ES
dc.contributor.author Vargas, Borja es_ES
dc.contributor.author Varela-Entrecanales, Manuel es_ES
dc.contributor.author Cuesta Frau, David es_ES
dc.date.accessioned 2017-06-15T09:00:41Z
dc.date.available 2017-06-15T09:00:41Z
dc.date.issued 2017-02
dc.identifier.issn 0883-9441
dc.identifier.uri http://hdl.handle.net/10251/82873
dc.description.abstract Body temperature monitoring provides healthcarers with key clinical information about the physiological status of patients. Temperature readings are taken periodically to detect febrile episodes and consequently implement the appropriate medical countermeasures. However, fever is often difficult to assess at early stages, or remains undetected until the next reading, probably a few hours later. The objective of this paper is to develop a statistical model to forecast fever before a temperature threshold is exceeded to improve the therapeutic approach to the subjects involved. To this end, temperature series of nine patients admitted to a general Internal Medicine ward were obtained with a continuous monitoring holter device, collecting measurements of peripheral and core temperature once per minute. These series were used to develop different statistical models that could quantify the probability of having a fever spike in the following 60 minutes. A validation series was collected to assess the accuracy of the models. Finally, the results were compared with the analysis of some series by experienced clinicians. Two different models were developed: a logistic regression model and a linear discrimination analysis model. Both of them exhibited a fever peak forecasting accuracy above 84%. When compared with experts assessment, both models identified 35 out of 36 fever spikes (97.2%). The models proposed are highly accurate in forecasting the appearance of fever spikes within a short period of time in patients with suspected or confirmed febrile related illnesses. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Critical Care es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Thermometry es_ES
dc.subject Fever es_ES
dc.subject Temperature monitoring es_ES
dc.subject Infectious diseases es_ES
dc.subject Entropy es_ES
dc.subject ApEn es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Statistical models for fever forecasting based on advanced body temperature monitoring es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jcrc.2016.09.013
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica es_ES
dc.description.bibliographicCitation Jordán Núñez, J.; Miró Martínez, P.; Vargas, B.; Varela-Entrecanales, M.; Cuesta Frau, D. (2017). Statistical models for fever forecasting based on advanced body temperature monitoring. Journal of Critical Care. 37:136-140. doi:10.1016/j.jcrc.2016.09.013 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.jcrc.2016.09.013 es_ES
dc.description.upvformatpinicio 136 es_ES
dc.description.upvformatpfin 140 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 37 es_ES
dc.relation.senia 318060 es_ES


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