Prediction of postpartum depression using multilayer perceptrons and pruning

Methods of Information in Medicine

Volumn 48, Issue 3, 2009, Pages 291-298

  Tortajada, S ^a   Garcia Gomez J M ^a   Vicente, J ^a   Sanjuan J ^b   De Frutos, R ^b   Martin Santos R ^c   Garcia Esteve L ^c   Gornemann, I ^d   Gutierrez Zotes A ^e   Canellas, F ^f   Carracedo, A ^g   Gratacos, M ^h   Guillamat, R ⁱ   Baca Garcia E ^j   Robles, M ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b UNIVERSITY OF VALENCIA   (Spain)

^c INST MUNIC D INVESTIGACIO MEDICA   (Spain)

^d HOSPITAL REGIONAL UNIVERSITARIO DE MÁLAGA   (Spain)

^e IISPV   (Spain)

^f HOSPITAL UNIVERSITARIO SON ESPASES   (Spain)

^g University Hospital of Santiago de Compostela   (Spain)

^h CENTRE FOR GENOMIC REGULATION CRG   (Spain)

ⁱ HOSPITAL DE SABADELL   (Spain)

^j CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE SALUD MENTAL CIBERSAM   (Spain)

more..

Author keywords

Multilayer perceptron; Neural network; Postpartum depression; Pruning

Indexed keywords

ADULT; ALGORITHM; ARTICLE; COHORT ANALYSIS; FEMALE; FORECASTING; HUMAN; NERVE CELL NETWORK; PROSPECTIVE STUDY; PUERPERAL DEPRESSION; SPAIN; STATISTICAL MODEL;

ADULT; ALGORITHMS; COHORT STUDIES; DEPRESSION, POSTPARTUM; FEMALE; FORECASTING; HUMANS; LOGISTIC MODELS; NERVE NET; PROSPECTIVE STUDIES; SPAIN;

EID: 67650483683     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME0562     Document Type: Article

References (46)

1. Oates MR, Cox JL, Neema S, Asten P, Glangeaud- Freudenthal N, Figueiredo B, et al. Postnatal depression across countries and cultures: a qualitative study. British Journal of Psychiatry 2004; 46 (Suppl): s10-s16.
2. O'Hara MW, Swain AM. Rates and risk of postnatal depression - a meta analysis. International Review of Psychiatry 1996; 8: 37-54.
3. Cooper PJ, Murray L. Prediction, detection and treatment of postnatal depression. Archives of Disease in Childhood 1997; 77: 97-99.
4. Beck CT. Predictors of postpartum depression: an update. Nursing Research 2001; 50: 275-285.
5. Kendler KS, Kuhn J, Prescott CA. The interrelationship of neuroticism, sex and stressful life events in the prediction of episodes of major depression. American Journal of Psychiatry 2004; 161: 631-636.
6. Bloch M, Daly RC, Rubinow DR. Endocrine factors in the etiology of postpartum depression. Comprehensive Psychiatry 2003; 44: 234-246.
7. Treloar SA, Martin NG, Bucholz KK, Madden PAF, Heath AC. Genetic influences on post-natal depressive symptoms: findings from an Australian twin sample. Psychological Medicine 1999; 29: 645-654.
8. Ross LE, Gilbert EM, Evans SE, Romach MK. Mood changes during pregnancy and the postpartum period: development of a biopsychosocial model. Acta Psychiatrica Scandinavica 2004; 109: 457-466.
9. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, et al. Influence of life stress on depression: moderation by a polimorphism in the 5-HTT gene. Science 2003; 301: 386-389.
10. th Annual Symposium on Computer Applications in Medical Care; 1988. pp 245-249.
11. Tandon R, Adak S, Kaye JA. Neural networks for longitudinal studies in Alzheimers disease. Artificial Intelligence in Medicine 2006; 36: 245-255.
12. Zhu J, Hazarika N, Chung-Tsoi A, Sergejew A. Classification of EEG signals using wavelet coefficients and an ANN. In: Pan Pacific Conference on Brain Electric Topography. Sydney, Australia; 1994. p 27.
13. Jefferson MF, Pendleton N, Lucas CP, Lucas SB, Horan MA. Evolution of artificial neural network architecture: prediction of depression after mania. Methods Inf Med 1998; 37: 220-225.
14. Berdia S, Metz JT. An artificial neural network stimulating performance of normal subjects and schizophrenics on the Wisconsin card sorting test. Artificial Intelligence in Medicine 1998; 13: 123-138.
15. Franchini L, Spagnolo C, Rossini D, Smeraldi E, Bellodi L, Politi E. A neural network approach to the outcome definition on first treatment with sertraline in a psychiatric population. Artificial Intelligence in Medicine 2001; 23: 239-248.
16. Sanjuán J, Martín-Santos R, García-Esteve L, Carot JM, Guillamat R, Gutiérrez-Zotes A, et al. Mood changes after delivery: role of the serotonin transporter gene. British Journal of Psychyatry 2008; 193: 383-388.
17. Vicente J, García-Gómez JM, Vidal C, Martí- Bonmatí L, del Arco A, Robles M. SOC: A distributed decision support architecture for clinical diagnosis. Biological and Medical Data Analysis; 2004 p. 96-104.
18. García-Esteve L, Ascaso L, Ojuel J, Navarro P. Validation of the Edinburgh Postnatal Depression Scale (EPDS) in Spanish mothers. Journal of Affective Disorders 2003; 75: 71-76.
19. Nurnberger JI, Blehar MC, Kaufmann C, York- Cooler C, Simpson S, Harkavy-Friedman J, et al. Diagnostic interview for genetic studies and training. Archives of Genetic Psychiatry 1994; 51: 849-859.
20. Roca M, Martin-Santos R, Saiz J, Obiols J, Serrano MJ, Torrens M, et al. Diagnostic Interview for Genetic Studies (DIGS): Inter-rater and test-retest reliability and validity in a Spanish population. European Psychiatry 2007; 22: 44-48.
21. Eysenck HJ, Eysenck SBG. The Eysenck Personality Inventory. London: University of London Press; 1964.
22. Aluja A, García O, García LF. A psychometric analysis of the revised Eysenck Personality Questionnaire short scale. Personality and Individual Differences 2003; 35: 449-460.
23. Paykel ES. Methodological aspects of life events research. Journal of Psychosomatic Research 1983; 27: 341-352.
24. Zalsman G, Huang YY, Oquendo MA, Burke AK, Hu XZ, Brent DA, et al. Association of a triallelic serotonin transporter gene promoter region (5-HTTLPR) polymorphism with stressful life events and severity of depression. American Journal of Psychiatry 2006; 163: 1588-1593.
25. Bellón JA, Delgado A, Luna JD, Lardelli P. Validity and reliability of the Duke-UNC-11 questionnaire of functional social support. Atención Primaria 1996; 18: 158-163.
26. Hranilovic D, Stefulj J, Schwab S, Borrmann-Hassenbach M, Albus M, Jernej B, et al. Serotonin transporter promoter and intron 2 polymorphisms: relationship between allelic variants and gene expression. Biological Psychiatry 2004; 55: 1090-1094.
27. Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 1958; 65 (6): 386-408.
28. Bishop CM. Neural Networks for Pattern Recognition. Oxford, UK: Clarendon Press; 1995.
29. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. The MIT Press 1986. pp 318-362.
30. Le Cun Y, Denker JS, Solla A. Optimal brain damage. Advances in Neural Information Processing Systems 1990; 2: 598-605.
31. Duda RO, Hart PE, Stork DG. Pattern Classification. New York, NY: Wiley-Interscience; 2001.
32. Mao J, Jain AK. Artificial neural networks for feature extraction and multivariate data projection. IEEE Transactions on Neural Networks. 1995; 6 (2): 296-317.
33. Leray P, Gallinari P. Feature selection with neural networks. Behaviormetrika 1999; 26: 145-166.
34. Hassibi B, Stork DG, Wolf G. Optimal brain surgeon and general network pruning. In: Proceedings of the 1993 IEEE International Conference on Neural Networks. San Francisco, CA; 1993. pp 293-300.
35. Hosmer DW, Lemeshow S. Applied logistic regression. Wiley-Interscience; 2000.
36. Kubat M, Matwin S. Addressing the curse of imbalanced training sets: one-sided selection. In: Proc. 14th International Conference on Machine Learning. Morgan Kaufmann; 1997. pp 179-186.
37. Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intelligent Data Analysis Journal 2002; 6 (5): 429-449.
38. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27 (8): 861-874.
39. Saad EW, Wunsch DC. Neural network explanation using inversion. Neural Networks. 2007; 20 (1): 78-93.
40. Heckerling PS, Gerber BS, Tape TG, Wigton RS. Entering the black box of neural networks. A descriptive study of clinical variables predicting community- acquired pneumonia. Methods Inf Med 2003; 42: 287-296.
41. Sakai S, Kobayashi K, Nakamura J, Toyabe S, Akazawa K. Accuracy in the diagnostic prediction of acute appendicitis based on the bayesian network model. Methods Inf Med 2007; 46: 723-726.
42. Camdeviren HA, Yazici AC, Akkus Z, Bugdayci R, Sungur MA. Comparison of logistic regression model and classification tree: an application to postpartum depression data. Expert Systems with Applications 2007; 32: 987-994.
43. Dennis CL. Psychosocial and psychological interventions for prevention of postnatal depression: systematic review. BMJ 2005; 331 (7507): 15.
44. Fieschi M, Dufour JC, Staccini P, Gouvernet J, Bouhaddou O. Medical Decision Support Systems: Old dilemmas and new paradigms? Methods Inf Med 2003; 42: 190-198.
45. Lisboa PJ, Taktak AFG. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks 2006; 19(4): 408-415.
46. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005. bmj.38398.500764.8F.