Predicting weaning difficulty for planned extubation patients with an artificial neural network
Predicting weaning difficulty for planned extubation patients with an artificial neural network
复制标题
DOI：
10.1097/md.0000000000017392
复制
发表时间：
2019-10-01
期刊：
影响因子：
1.6
通讯作者：
Chou, Willy
中科院分区：
文献类型：
--
作者：
Hsieh, Meng Hsuen;Hsieh, Meng Ju;Chou, Willy
本研究以某医疗中心8个成人ICU中计划拔管患者为研究对象，对2009年12月至2011年12月奇美医学中心ICU中3602例计划拔管患者的数据进行了队列观察，并对人工神经网络(ANN)模型进行了训练和检验。输入特征包含47个临床风险因素，输出分为三类：简单、困难和长时间脱机。建立了具有4个隐含层的神经网络模型，每层隐含30个神经元。模型精度为0.769，简单脱机、长时间脱机和困难脱机的受试者操作特征曲线下面积分别为0.910、0.849和0.942。结果表明，人工神经网络模型对计划拔管患者脱机难度有较好的预测效果。该模型将有助于预测ICU患者成功的计划拔管。
This study aims to construct a neural network to predict weaning difficulty among planned extubation patients in intensive care units.This observational cohort study was conducted in eight adult ICUs in a medical center about adult patients experiencing planned extubation.The data of 3602 patients with planned extubation in ICUs of Chi-Mei Medical Center (from Dec. 2009 through Dec. 2011) was used to train and test an artificial neural network (ANN) model. The input features contain 47 clinical risk factors and the outputs are classified into three categories: simple, difficult, and prolonged weaning. A deep ANN model with four hidden layers of 30 neurons each was developed. The accuracy is 0.769 and the area under receiver operating characteristic curve for simple weaning, prolonged weaning, and difficult weaning are 0.910, 0.849, and 0.942 respectively.The results revealed that the ANN model achieved a good performance in prediction the weaning difficulty in planned extubation patients. Such a model will be helpful for predicting ICU patients' successful planned extubation.