Predicting Developmental Disorder in Infants Using an Artificial Neural Network
Abstract
Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother-child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant's development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.
Council on Children With Disabilities; Section on Developmental Behavioral Pediatrics; Bright Futures Steering Committee; Medical Home Initiatives for Children With Special Needs Project Advisory Committee. Identifying infants and young children with developmental disorders in the medical home: An algorithm for developmental surveillance and screening. Pediatrics 2006;118(1):405-20.
Rydz D, Srour M, Oskoui M, Marget N, Shiller M, Birnbaum R, Majnemer A, Shevell M. Screening for developmental delay in setting of a community pediatricclinic: A prospective assessment of Parent–ReportQuestionnaires. Pediatrics 2006;118(4):e1178-86.
Williams J, Holmes CA. Improving the early detection of children with subtle developmental problems. J Child Health Care 2004;8(1):34-46.
Frances P, Galascoe FP, Kevin P. Developmental- Behavioral Screening and Surveillance. In: Kliegman RM, Stanton BF, St. Geme JW, Schor NF, Behrman RE. Nelson Textbook of Pediatrics. 19th ed. Philadelphia: Saunders. 2011; p: 39-45.
Glascoe FP, Shapiro HL. Introduction to developmental and behavioral screening.http://www.dbpeds.org/articles/detail.cfm?id=5.
Mayson TA, Harris SR, Bachman CL. Gross Motor Development of Asian and European Children on four Motor Assessments: A Literature Review. Pediatr Phys Ther 2007;19(2):148-53.
Crick F. The recent excitement about neural networks. Nature 1989;337(6207):129–32.
Hinton G. How neural networks learn from experience. Sci Am 1992;267(3):145-51.
Tu J. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996;49(11):1225–31.
Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995;346(8983):1135–38.
Hertz J, Krogh A, Palmer R. Introduction to the theory of neural computing. Massachusetts : Addison Wesley; 1991.
Rumelhart DE, McClelland JL, Rumelhart DE, Hinton GE, Williams RJ. Learning internal representation by error propagation in Parallel distributed processing: Explorationin the microstructure of cognition. In: Rumelhart DE,McClelland JL. Massachusetts: MIT Press 1986; p.318–64.
Baxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 1996;347(8993):12–5.
Cross SS, Harrison RF, Kennedey RL. Introduction to neural networks. Lancet 1995;346(8982):1075–9.
Dybowski R, Gant V. Artificial neural networks in pathology and medical laboratories. Lancet1995;346(8984):1203-7.
Forsström JJ, Dalton KJ. Artificial neural networks for decision support in clinical medicine. Ann Med 1995;27(5):509–17.
Smith M. Neural networks for statistical modeling. New York: Van Nostrand Reinhold; 1993.
Soleimani F, Vameghi R, Hemmati S, Salman-Roghani R. Perinatal and neonatal risk factors for neurodevelopmental outcome in infants in Karaj. Arch Iranian Med 2009; 12(2):135–9.
Ronald B, David MD. Child and Adolescent Neurology. USA: Mosby 1998; p. 15–54.
Soleimani F, Dadkhah A. Validity and reliability of Infant Neurological International Battery for detection of gross motor developmental delay in Iran. Child Care Health andDev 2007;33(3):262–5.
Drew PJ, Monson JR. Artificial intelligence for clinicians. J R Soc Med 1999;92(3):108–9.
Rumelhart DE, Hinton GE, Williams RJ. Learningrepresentations by back-propagating errors. Nature 1986;323:533-6.
Wachs TD. Necessary but not sufficient: the respective roles of single and multiple influences on individualdevelopment, American Psychological Association, Washington DC; 2000.
Jack P. Shonkoff, Deborah Ph. Committee on Integrating the Science of Child Development: From neurons to neighborhoods: the science of child development, National Academy Press, Washington DC; 2000.
RA Thompson and CA Nelson, Developmental science and the media: early brain development. Am Psychol2001;56(1):5–15.
Black JE, Jones TA, Nelson CA, Greenough WT. Neuronal plasticity and the developing brain. Handbook of Child and Adolescent Psychiatry. vol 1. New York:Wiley; 1998. P. 31–53
Bredy TW, Humpartzoomian RA, Cain DP, Meaney MJ. Partial reversal of the effect of maternal care on cognitive function through environmental enrichment. Neuro Sci 2003;118(2):571-6.
English and Romanian Adoptees (ERA) Study Team, M Rutter and T O'Connor, Are there biological programming effects for psychological development? Findings from a study of Romanian adoptees. Dev Psychol 2004;40(1): 81-94.
Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008;371(9606):75–84.
Moore ML. Preterm labor and birth: what have we learned in the past two decades? J Obstet Gynecol Neonatal Nurs 2003;32(5):638–9.
Romero R, Espinoza J, Kusanovic JP, Gotsch F, Hassan S, Erez O, Chaiworapongsa T, Mazor M. The preterm parturition syndrome. Brit J Obstet Gyna 2006;113 (Supple 3):17–42.
American Academy of Pediatrics - Committee on Children with Disabilities. Developmental Surveillance and Screening of Infants and Young Children. Pediatrics 2001;108(1):192-5.
Soleimani F, Vameghi R, Biglarian A, Daneshmandan N. Risk factors associated with cerebral palsy in children born in eastern and northern districts of Tehran. IRCMJ, 2010;12(4):428-33.
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| Issue | Vol 51, No 6 (2013) | |
| Section | Articles | |
| Keywords | ||
| Infant Risk factor Neural network Developmental disability Prognosis | ||
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