决策树的欠采样和装袋在极早产儿呼吸行为分析中的应用
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
摘要：
极早产儿在生命的最初几天通常需要气管插管和机械通气。由于长时间有创机械通气（IMV）的有害影响，临床医生的目标是一旦他们认为婴儿准备好就拔管。不幸的是，现有的预测拔管准备的策略因临床医生和机构而异，并导致高的再插管率。我们提出了一种方法，使用随机森林分类器的心肺变异性的分析，以预测拔管准备。我们解决了数据不平衡的问题，采用随机欠采样的例子，从大多数类之前，在一个袋子里训练每个决策树。通过结合临床领域知识，我们进一步证明，我们的分类器可以识别71%的拔管失败的婴儿，同时保持78%的成功检测率。
英文摘要：
Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready. Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
