Machine learning to predict extubation outcome in premature infants.
Machine learning to predict extubation outcome in premature infants.
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DOI：
10.1109/ijcnn.2013.6707058
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Almeida JS
中科院分区：
文献类型：
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作者：
Mueller M;Wagner CC;Stanislaus R;Almeida JS
尽管在过去的几十年中，对通气早产儿的治疗取得了许多进展，但确定这些婴儿拔管的最佳时间点仍然具有挑战性，拔管失败的发生率基本不变。其目的是为临床医生提供决策支持工具，以确定是否通过使用一组机器学习算法对486名接受机械通气的早产儿进行数据集的机械通气早产儿进行拔管。算法包括人工神经网络（ANN）、支持向量机（SVM）、朴素贝叶斯分类器（NBC）、提升决策树（BDT）和多变量逻辑回归（MLR）。ANN、MLR和NBC的结果令人满意（曲线下面积[AUC]：0.63-0.76）;然而，SVM和BDT始终表现出较差的性能（AUC ~0.5）。复杂的医疗数据，如用于本研究的数据集，需要进一步的预处理步骤，然后才能开发出预测模型，实现类似或更好的性能比临床医生。
Though treatment of the ventilated premature infant has experienced many advances over the past decades, determining the best time point for extubation of these infants remains challenging and the incidence of extubation failures largely unchanged. The objective was to provide clinicians with a decision-support tool to determine whether to extubate a mechanically ventilated premature infant by using a set of machine learning algorithms on a dataset assembled from 486 premature infants receiving mechanical ventilation. Algorithms included artificial neural networks (ANN), support vector machine (SVM), naïve Bayesian classifier (NBC), boosted decision trees (BDT), and multivariable logistic regression (MLR). Results for ANN, MLR, and NBC were satisfactory (area under the curve [AUC]: 0.63–0.76); however, SVM and BDT consistently showed poor performance (AUC ~0.5). Complex medical data such as the data set used for this study require further preprocessing steps before prediction models can be developed that achieve similar or better performance than clinicians.