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Methods Inf Med 2009; 48(03): 291-298
DOI: 10.3414/ME0562
Original Articles
Schattauer GmbH

Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning

S. Tortajada
1   IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
,
J. M. García-Gómez
1   IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
,
J. Vicente
1   IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
,
J. Sanjuán
2   Faculty of Medicine, Universidad de Valencia, Valencia CIBERSAM, Spain
,
R. de Frutos
2   Faculty of Medicine, Universidad de Valencia, Valencia CIBERSAM, Spain
,
R. Martín-Santos
3   IMIM-Hospital del Mar and ICN-Hospital Clínic, Barcelona CIBERSAM, Spain
,
L. García-Esteve
3   IMIM-Hospital del Mar and ICN-Hospital Clínic, Barcelona CIBERSAM, Spain
,
I. Gornemann
4   Hospital Carlos Haya, Málaga, Spain
,
A. Gutiérrez-Zotes
5   Hospital Pere Mata, Reus, Spain
,
F. Canellas
6   Hospital Son Dureta, Palma de Mallorca, Spain
,
Á. Carracedo
7   National Genotyping Center, Hospital Clínico, Santiago de Compostela, Spain
,
M. Gratacos
8   Center for Genomic Regulation, CRG, Barcelona, Spain
,
R. Guillamat
9   Hospital Parc Tauli, Sabadell, Spain
,
E. Baca-García
10   Hospital Jiménez Díaz, Madrid CIBERSAM, Spain
,
M. Robles
1   IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 15. Mai 2008

accepted: 08. März 2008

Publikationsdatum:
17. Januar 2018 (online)

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Summary

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.

Keywords

Multilayer perceptron - neural network - pruning - postpartum depression
 

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