Please use this identifier to cite or link to this item: http://hdl.handle.net/10668/484
Title: Prediction of postpartum depression using multilayer perceptrons and pruning.
Authors: Tortajada, Salvador
García-Gomez, Juan M.
Vicente, Javier
Sanjuán, Julio
de Frutos, Rosa
Martín-Santos, Rocío
García-Esteve, Luisa
Gornemann, Isolde
Gutiérrez-Zotes, Alfonso
Canellas, Francesca
Carracedo, Angel
Gratacos, Monica
Guillamat, Roser
Baca-García, Enrique
Robles, Montserrat
metadata.dc.contributor.authoraffiliation: [Tortajada,S; García-Gomez,JM; Vicente,J; Robles,M] IBIME,Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas(ITACA), Universidad Politécnica de Valencia, Valencia, Spain. [Sanjuán,J; de Frutos,R] Faculty of Medicine, Universidad de Valencia, Valencia CIBERSAM, Spain. [Martín-Santos,R; García-Esteve,L] IMIM-Hospital del Mar and ICN-Hospital Clínic, Barcelona CIBERSAM, Spain. [Gornemann,I] Hospital Carlos Haya, Málaga, Spain. [Gutiérrez-Zotes,A] Hospital Pere Mata, Reus, Spain. [Canellas,F] Hospital Son Dureta, Palma de Mallorca, Spain.[Carracedo,A] National Genotyping Center, Hospital Clínico, Santiago de Compostela, Spain. [Gratacos,M] Center for Genomic Regulation, CRG, Barcelona, Spain. [Guillamat,R] Hospital Parc Tauli, Sabadell, Spain. [Baca-García,E] Hospital Jiménez Díaz, Madrid CIBERSAM, Spain.
Keywords: Multilayer perceptron;Neural network;Pruning;Postpartum depression;España;Adulto;Algoritmos;Estudios de cohortes;Depresión postparto;Femenino;Predicción;Humanos;Modelos logísticos;Red nerviosa;Estudios prospectivos
metadata.dc.subject.mesh: Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithms
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies
Medical Subject Headings::Psychiatry and Psychology::Mental Disorders::Mood Disorders::Depressive Disorder::Depression, Postpartum
Medical Subject Headings::Check Tags::Female
Medical Subject Headings::Disciplines and Occupations::Social Sciences::Forecasting
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Models, Theoretical::Models, Statistical::Logistic Models
Medical Subject Headings::Anatomy::Nervous System::Nerve Net
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies::Prospective Studies
Medical Subject Headings::Geographicals::Geographic Locations::Europe::Spain
Medical Subject Headings::Named Groups::Persons::Age Groups::Adult
Issue Date: 2009
Publisher: Schattauer
Citation: Tortajada S, García-Gomez JM, Vicente J, Sanjuán J, de Frutos R, Martín-Santos R, et al. Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf Med 2009; 48(3):291-8
Abstract: OBJECTIVE. The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians. MATERIALS AND METHODS. Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy. RESULTS. Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression. CONCLUSIONS. The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.
Description: Journal Article; Research Support, Non-U.S. Gov't;
URI: http://hdl.handle.net/10668/484
metadata.dc.relation.publisherversion: http://www.schattauer.de/en/magazine/subject-areas/journals-a-z/methods/contents/archive/issue/663/manuscript/11260.html
metadata.dc.identifier.doi: 10.3414/ME0562
ISSN: 0026-1270 (Print)
Appears in Collections:01- Artículos - Hospital Regional de Málaga

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