Prediction of periventricular leukomalacia occurrence in neonates after heart surgery.
Prediction of periventricular leukomalacia occurrence in neonates after heart surgery.
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DOI：
10.1109/jbhi.2013.2285011
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发表时间：
2014-07
影响因子：
7.7
通讯作者：
Nataraj C
中科院分区：
文献类型：
--
作者：
Jalali A;Buckley EM;Lynch JM;Schwab PJ;Licht DJ;Nataraj C
本文旨在探讨利用新生儿心脏手术后12小时内收集的生命和血气数据预测脑室周围白质软化症（PVL）的发生。已采用数据挖掘方法生成一组规则，用于将受试者分类为健康或PVL受影响。鉴于血气和生命数据具有不同的采样率，在本研究中，我们将数据分为两类：（i）高分辨率（生命）和（ii）低分辨率（血气），并基于每个数据类别设计了单独的分类器。所开发的算法是由几个阶段组成，首先，已从每个数据类别中提取的特征池和提取的功能已排名的基础上的数据的可靠性和它们的互信息内容与输出。一个最佳的特征子集具有最高的鉴别能力已形成使用同时最大化的类可分性度量和互信息的集合。两个独立的决策树（DT）已经开发用于分类目的，更重要的是发现数据之间存在的隐藏关系，以帮助我们更好地了解PVL病理生理学。DT结果表明，生命数据中的高幅度20分钟变化和低样本熵以及定义的范围外指标以及血气数据中的最大变化率是PVL预测的重要因素。低样本熵表示血流动力学测量缺乏变异性，并且具有小波动的恒定血压是PVL发生的重要指标。最后，使用不同的时间框架的数据收集，我们表明，前六个小时的数据包含足够的信息PVL发生预测。
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 twelve hours after 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: (i) high resolution (vital), and (ii) 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 (DT) 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 twenty minute 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 six hours of data contain sufficient information for PVL occurrence prediction.