[Blood gases values forecasting by artificial neural network in prematurely born infants with respiratory distress]

Artificial Neural Networks (ANN) are modern tools for data analysis. They can compete with commonly used statistical methods. Till the date they were not used for modeling continuous parameters in medicine. The aim of this paper is to assess accuracy of ANN in forecasting blood gases values in preterm infants with respiratory distress. The study was conducted with the use of the data from Neonatal Information System (NIS) describing first 30 days of the hospital stay of 19 newborns with birthweight < or = 1500 g admitted to the unit before second day of life in 1997. Separate ANN was created for prognosis of 4 parameters of arterial blood gases (pH, pCO2, pO2, HCO3) and oxygenation index (pO2/FiO2) for 1 hour in advance. For each forecasted parameter input set of parameters was established. The ANN was taught with back propagation algorithm using data of 18 patients, the accuracy of prognosis was tested on the data of the remaining patient. This procedure was repeated 19 times. The error of prognosis was assessed in comparison to predefined curves of the maximal error. The accuracy of achieved prognoses was respectively for pH, pCO2, pO2, HCO3, pO2/FiO2 98.9, 99.7, 97.1, 98.3, 95%.