title: Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.
abstract: BACKGROUND
  The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness.
  
  
  METHODS
  Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH.
  
  
  RESULTS
  For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability.
  
  
  CONCLUSIONS
  ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.
authors.affiliation: Department of Endocrinology and Metabolic Diseases, Polish Mother's Memorial Hospital - Research Institute, Lodz, Poland.
authors.forename: Joanna
authors.initials: J
authors.lastname: Smyczynska
authors.affiliation: Department of Endocrinology and Metabolic Diseases, Polish Mother's Memorial Hospital - Research Institute, Lodz, Poland.
authors.forename: Maciej
authors.initials: M
authors.lastname: Hilczer
authors.affiliation: AGH University of Science and Technology, Department of Automatics and Biomedical Engineering, Krakow, Poland.
authors.forename: Urszula
authors.initials: U
authors.lastname: Smyczynska
authors.affiliation: Department of Endocrinology and Metabolic Diseases, Polish Mother's Memorial Hospital - Research Institute, Lodz, Poland.
authors.forename: Renata
authors.initials: R
authors.lastname: Stawerska
authors.affiliation: AGH University of Science and Technology, Department of Automatics and Biomedical Engineering, Krakow, Poland.
authors.forename: Ryszard
authors.initials: R
authors.lastname: Tadeusiewicz
authors.affiliation: Department of Endocrinology and Metabolic Diseases, Polish Mother's Memorial Hospital - Research Institute, Lodz, Poland.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Lewinski
mesh_terms: D000293:Adolescent; D001827:Body Height / Q000187:drug effects; D002648:Child; D004392:Dwarfism / Q000188:drug therapy*; D005260:Female; D013006:Growth Hormone / Q000008:administration & dosage / Q000172:deficiency* / Q000494:pharmacology*; D019382:Human Growth Hormone / Q000172:deficiency; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011994:Recombinant Proteins
chemical_list: D011994:Recombinant Proteins; D019382:Human Growth Hormone; D013006:Growth Hormone