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"resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Comparar la capacidad de predicción de mortalidad hospitalaria de una red neuronal artificial (RNA) con el Acute Physiology and Chronic Health Evaluation II (APACHE II) y la regresión logística (RL), y comparar la asignación de probabilidades entre los distintos modelos.</p> <span class="elsevierStyleSectionTitle">Método</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Se recogen de forma prospectiva las variables necesarias para el cálculo del APACHE II. Disponemos de 1.146 pacientes asignándose aleatoriamente (70 y 30%) al grupo de Desarrollo (800) y al de Validación (346). Con las mismas variables se genera un modelo de RL y de RNA (perceptrón de 3 capas entrenado por algoritmo de <span class="elsevierStyleItalic">backpropagation</span> con remuestreo <span class="elsevierStyleItalic">bootstrap</span> y con 9 nodos en la capa oculta) en el grupo de desarrollo. Se comparan los tres modelos en función de los criterios de discriminación con el área bajo la curva ROC (ABC [IC del 95%]) y de calibración con el test de Hosmer- Lemeshow C (HLC). Las diferencias entre las probabilidades se valoran con el test de Bland-Altman.</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">En el grupo de validación, el APACHE II con ABC de 0,79 (0,75-0,84) y HLC de 11 (p = 0,329); modelo RL, ABC de 0,81 (0,76-0,85) y HLC de 29 (p = 0,0001), y en RNA, ABC de 0,82 (0,77-0,86) y HLC de 10 (p = 0,404). Los pacientes con mayores diferencias en la asignación de probabilidad entre RL y RN (8% del total) son pacientes con problemas neurológicos. Los peores resultados se obtienen en los pacientes traumáticos (ABC inferior a 0,75 en todos los modelos). En los pacientes respiratorios, la RNA alcanza los mejores resultados (ABC = 0,87 [0,78-0,91]).</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Una RNA es capaz de estratificar el riesgo de mortalidad hospitalaria utilizando las variables del sistema APACHE II. La RNA consigue mejores resultados frente a RL, sin alcanzar significación, ya que no trabaja con restricciones lineales ni de independencia de variables, con una diferente asignación de probabilidad individual entre los modelos.</p>"
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"titulo" => "Abstract"
"resumen" => "<span class="elsevierStyleSectionTitle">Objective</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">To compare the ability of an artificial neural network (ANN) to predict hospital mortality with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) system and multiple logistic regression (LR). A secondary objective was to compare the allocation of individual probability among the models.</p> <span class="elsevierStyleSectionTitle">Method</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The variables required for calculating the APACHE II were prospectively collected. A total of 1146 patients were divided (randomly 70% and 30%) into the Development (800) and the Validation (346) sets. With the same variables an LR model and an ANN were carried out (a 3-layer perceptron trained by algorithm backpropagation with bootstrap resampling and with 9 nodes in the hidden layer) in the Development set. The models developed were contrasted with the Validation set and their discrimination properties were evaluated using the area under the ROC curve (AUC [95% CI]) and calibration with the Hosmer-Lemeshow C (HLC) test. Differences between the probabilities were evaluated using the Bland-Altman test.</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The Validation set showed an APACHE II with an AUC = 0.79 (0.75-0.84) and HLC = 11 (p = 0.329); LR model AUC = 0.81 (0.76-0.85) and HLC = 29 (p = 0.0001) and an ANN AUC = 0.82 (0.77-0.86) and HLC = 10 (p = 0.404). The patients with the most important differences in the allocation of probability between LR and ANN (8% of the total) were neurological. The worst results were found in trauma patients with an AUC of not greater than 0.75 in all the models. In respiratory patients, the ANN achieved the best AUC = 0.87 (0.78- 0.91).</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">The ANN was able to stratify hospital mortality risk by using the APACHE II system variables. The ANN tended to achieve better results than LR, since, in order to work, it does not require lineal restrictions or independent variables. Allocation of individual probability differed in each model.</p>"
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