Application of Decision Tree in the Prediction of Periventricular Leukomalacia (PVL) Occurrence in Neonates After Neonatal Heart Surgery

摘要

This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters.