Aplicación de las redes neuronales artificiales para la estratificación de riesgo de mortalidad hospitalaria
Application of artificial neural networks for risk stratification of hospital mortality
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Carles" "autores" => array:1 [ 0 => array:2 [ "nombre" => "M." "apellidos" => "Carles" ] ] ] ] ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S021391110371797X?idApp=WGSE" "url" => "/02139111/0000001700000006/v1_201310251604/S021391110371797X/v1_201310251604/es/main.assets" ] "es" => array:14 [ "idiomaDefecto" => true "titulo" => "Aplicación de las redes neuronales artificiales para la estratificación de riesgo de mortalidad hospitalaria" "tieneTextoCompleto" => 0 "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "504" "paginaFinal" => "511" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "J. Trujillano, J. March, M. Badia, A. Rodríguez, A. Sorribas" "autores" => array:5 [ 0 => array:4 [ "nombre" => "J." "apellidos" => "Trujillano" "email" => array:1 [ 0 => "jtruji@cmb.udl.es" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "J." "apellidos" => "March" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "M." "apellidos" => "Badia" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "A." "apellidos" => "Rodríguez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "A." "apellidos" => "Sorribas" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Unidad de Cuidados Intensivos. Hospital Universitario Arnau de Vilanova de Lleida. Lleida" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Departamento de Ciencias Médicas Básicas. Universidad de Lleida. Lleida. España" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "*" "correspondencia" => "Correspondencia: Hospital Arnau de Vilanova. Unidad de Cuidados Intensivos. Avda. Rovira Roure, 80. 28198 Lleida. España." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Application of artificial neural networks for risk stratification of hospital mortality" ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2003-03-31" "fechaAceptado" => "2003-09-15" "PalabrasClave" => array:2 [ "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec268698" "palabras" => array:5 [ 0 => "Mortalidad hospitalaria" 1 => "Estratificación de riesgo" 2 => "Unidad de cuidados intensivos" 3 => "Redes neuronales artificiales" 4 => "<span class="elsevierStyleItalic">Bootstrap</span>" ] ] ] "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Key words" "identificador" => "xpalclavsec268697" "palabras" => array:5 [ 0 => "Mortality" 1 => "Risk assessment" 2 => "Intensive Care Unit" 3 => "Artificial Neural Network" 4 => "Bootstrap" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "es" => array:2 [ "titulo" => "Resumen" "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>" ] "en" => array:2 [ "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>" ] ] "bibliografia" => array:2 [ "titulo" => "Bibliografía" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:36 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1." "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Análisis automatizado de la calidad del conjunto mínimo de datos básicos. Implicaciones para los sistemas de ajuste de riesgos" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "J. Librero" 1 => "R. Ordiñana" 2 => "S. 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| año/Mes | Html | Total | |
|---|---|---|---|
| 2024 Noviembre | 4 | 4 | 8 |
| 2024 Octubre | 56 | 40 | 96 |
| 2024 Septiembre | 52 | 22 | 74 |
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| 2023 Diciembre | 32 | 14 | 46 |
| 2023 Noviembre | 34 | 23 | 57 |
| 2023 Octubre | 31 | 12 | 43 |
| 2023 Septiembre | 48 | 15 | 63 |
| 2023 Agosto | 32 | 16 | 48 |
| 2023 Julio | 42 | 19 | 61 |
| 2023 Junio | 35 | 15 | 50 |
| 2023 Mayo | 31 | 13 | 44 |
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| 2023 Marzo | 39 | 34 | 73 |
| 2023 Febrero | 28 | 10 | 38 |
| 2023 Enero | 27 | 16 | 43 |
| 2022 Diciembre | 46 | 28 | 74 |
| 2022 Noviembre | 46 | 39 | 85 |
| 2022 Octubre | 34 | 26 | 60 |
| 2022 Septiembre | 25 | 21 | 46 |
| 2022 Agosto | 37 | 44 | 81 |
| 2022 Julio | 26 | 41 | 67 |
| 2022 Junio | 28 | 29 | 57 |
| 2022 Mayo | 29 | 42 | 71 |
| 2022 Abril | 37 | 34 | 71 |
| 2022 Marzo | 44 | 32 | 76 |
| 2022 Febrero | 33 | 32 | 65 |
| 2022 Enero | 28 | 30 | 58 |
| 2021 Diciembre | 26 | 38 | 64 |
| 2021 Noviembre | 22 | 58 | 80 |
| 2021 Octubre | 36 | 27 | 63 |
| 2021 Septiembre | 43 | 45 | 88 |
| 2021 Agosto | 36 | 34 | 70 |
| 2021 Julio | 35 | 25 | 60 |
| 2021 Junio | 34 | 24 | 58 |
| 2021 Mayo | 43 | 26 | 69 |
| 2021 Abril | 86 | 52 | 138 |
| 2021 Marzo | 77 | 22 | 99 |
| 2021 Febrero | 26 | 23 | 49 |
| 2021 Enero | 32 | 28 | 60 |
| 2020 Diciembre | 40 | 19 | 59 |
| 2020 Noviembre | 31 | 12 | 43 |
| 2020 Octubre | 25 | 11 | 36 |
| 2020 Septiembre | 18 | 15 | 33 |
| 2020 Agosto | 22 | 14 | 36 |
| 2020 Julio | 19 | 16 | 35 |
| 2020 Junio | 32 | 8 | 40 |
| 2020 Mayo | 54 | 11 | 65 |
| 2020 Abril | 22 | 15 | 37 |
| 2020 Marzo | 8 | 7 | 15 |
| 2020 Febrero | 33 | 12 | 45 |
| 2020 Enero | 16 | 15 | 31 |
| 2019 Diciembre | 20 | 10 | 30 |
| 2019 Noviembre | 15 | 7 | 22 |
| 2019 Octubre | 12 | 6 | 18 |
| 2019 Septiembre | 17 | 5 | 22 |
| 2019 Agosto | 12 | 7 | 19 |
| 2019 Julio | 14 | 9 | 23 |
| 2019 Junio | 8 | 7 | 15 |
| 2019 Mayo | 15 | 13 | 28 |
| 2019 Abril | 9 | 18 | 27 |
| 2019 Marzo | 8 | 10 | 18 |
| 2019 Febrero | 19 | 12 | 31 |
| 2019 Enero | 4 | 6 | 10 |
| 2018 Diciembre | 3 | 9 | 12 |
| 2018 Noviembre | 7 | 12 | 19 |
| 2018 Octubre | 31 | 20 | 51 |
| 2018 Septiembre | 19 | 14 | 33 |
| 2018 Agosto | 18 | 10 | 28 |
| 2018 Julio | 24 | 9 | 33 |
| 2018 Junio | 23 | 9 | 32 |
| 2018 Mayo | 26 | 12 | 38 |
| 2018 Abril | 14 | 4 | 18 |
| 2018 Marzo | 13 | 8 | 21 |
| 2018 Febrero | 14 | 5 | 19 |
| 2018 Enero | 13 | 5 | 18 |
| 2017 Diciembre | 15 | 13 | 28 |
| 2017 Noviembre | 20 | 11 | 31 |
| 2017 Octubre | 12 | 11 | 23 |
| 2017 Septiembre | 15 | 17 | 32 |
| 2017 Agosto | 12 | 8 | 20 |
| 2017 Julio | 22 | 17 | 39 |
| 2017 Junio | 20 | 13 | 33 |
| 2017 Mayo | 22 | 6 | 28 |
| 2017 Abril | 28 | 10 | 38 |
| 2017 Marzo | 20 | 24 | 44 |
| 2017 Febrero | 12 | 3 | 15 |
| 2017 Enero | 7 | 4 | 11 |
| 2016 Diciembre | 45 | 2 | 47 |
| 2016 Noviembre | 55 | 10 | 65 |
| 2016 Octubre | 44 | 13 | 57 |
| 2016 Septiembre | 49 | 7 | 56 |
| 2016 Agosto | 56 | 14 | 70 |
| 2016 Julio | 28 | 6 | 34 |
| 2016 Marzo | 1 | 0 | 1 |
| 2016 Febrero | 1 | 0 | 1 |
| 2015 Diciembre | 1 | 0 | 1 |
| 2015 Octubre | 2 | 0 | 2 |
| 2015 Abril | 1 | 0 | 1 |
| 2015 Febrero | 1 | 0 | 1 |
| 2014 Diciembre | 4 | 0 | 4 |
| 2014 Noviembre | 2 | 0 | 2 |
| 2014 Octubre | 2 | 0 | 2 |
| 2014 Septiembre | 3 | 0 | 3 |
| 2014 Agosto | 5 | 0 | 5 |
| 2014 Julio | 9 | 0 | 9 |
| 2014 Junio | 6 | 0 | 6 |
| 2014 Mayo | 3 | 0 | 3 |
| 2014 Abril | 4 | 1 | 5 |
| 2014 Marzo | 3 | 2 | 5 |
| 2014 Febrero | 7 | 1 | 8 |
| 2014 Enero | 4 | 0 | 4 |
| 2013 Diciembre | 7 | 0 | 7 |
| 2013 Noviembre | 24 | 2 | 26 |
| 2013 Octubre | 24 | 2 | 26 |
| 2013 Septiembre | 16 | 3 | 19 |
| 2013 Agosto | 27 | 4 | 31 |
| 2013 Julio | 25 | 3 | 28 |
| 2003 Noviembre | 1287 | 0 | 1287 |
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