IEEE J Biomed Health Inform - Prediction of periventricular leukomalacia occurrence in neonates after heart surgery.

Tópicos

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Resumo

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.

Resumo Limpo

paper concern predict occurr periventricular leukomalacia pvl use vital blood gas data collect period h neonat cardiac surgeri data mine approach employ generat set rule classif subject healthi pvl affect view fact blood gas vital data differ sampl rate studi divid data two categori high resolut vital low resolut blood gas design separ classifi base data categori develop algorithm compos sever stage first featur pool extract data categori extract featur rank base data reliabl mutual inform content output optim featur subset highest discrimin capabl form use simultan maxim class separ measur mutual inform set two separ decis tree dts develop classif purpos import discov hidden relationship exist among data help us better understand pvl pathophysiolog dt result show high amplitud min variat low sampl entropi vital data defin rang index well maximum rate chang blood gas data import factor pvl predict low sampl entropi repres lack variabl hemodynam measur constant blood pressur small fluctuat import indic pvl occurr final use differ time frame data collect show first h data contain suffici inform pvl occurr predict

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