Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

• Published in: IEEE journal of biomedical and health informatics 2014-07, Vol.18 (4), p.1453-1460
• Main Authors: Jalali, Ali, Buckley, Erin M., Lynch, Jennifer M., Schwab, Peter J., Licht, Daniel J., Nataraj, C.
• Format: Article
• Language: English
• Subjects: Algorithms; Biomedical measurement; Blood; Blood Gas Analysis; Cardiac Surgical Procedures - statistics & numerical data; Classification; Data Mining; decision tree (DT); Decision Trees; Diagnosis, Computer-Assisted - methods; Feature extraction; feature ranking; Female; Humans; Infant, Newborn; Leukomalacia, Periventricular - diagnosis; Male; Mutual information; Pediatrics; periventricular leukomalacia (PVL); Reliability; ROC Curve; Signal Processing, Computer-Assisted; Vital Signs
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2013.2285011

This paper is concerned with predicting the occurrence of periventricular leukomalacia (PVL) using vital and blood gas data which are collected over a period of 12 h after the neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: 1) high resolution (vital), and 2) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DTs) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude 20 min variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first 6 h of data contain sufficient information for PVL occurrence prediction.