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Delirium prediction in the intensive care unit: a temporal approach

机译:重症监护室的妄预测:一种暂时性方法

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The incidence of delirium in intensive care units is high and associated with poor outcomes; therefore, its prediction is desirable to establish preventive treatments. This retrospective study proposes a novel approach for delirium prediction. We analyzed static and temporal data from 10,475 patients admitted to one of 15 intensive care units (ICUs) in Alberta, Canada between January 1, 2014 and June 30, 2016. We tested 168 different combinations of study design parameters and five different predictive models (logistic regression, support vector machines, random forests, adaptive boosting and neural networks). The area under the receiver operating characteristic curve (AUROC) ranged from 0.754 (CI 95% ± 0.018) to 0.852 (± 0.033), with sensitivity and specificity respectively ranging from 0.739 (CI 95% ± 0.047) to 0.840 (CI 95% ± 0.064), and 0.770 (CI 95% ± 0.030) to 0.865 (CI 95% ± 0.038). These results are similar to previous studies; however, our approach allows for continuous updates and short-term prediction horizons which might provide major advantages.
机译:重症监护病房的incidence妄发生率很高,并且预后差。因此,其预测对建立预防性治疗是可取的。这项回顾性研究提出了一种ir妄预测的新方法。我们分析了2014年1月1日至2016年6月30日期间在加拿大艾伯塔省15个重症监护病房(ICU)中入院的10475名患者的静态和时间数据。我们测试了168种研究设计参数组合和5种不同的预测模型(逻辑回归,支持向量机,随机森林,自适应增强和神经网络)。接收器工作特性曲线(AUROC)下的面积范围为0.754(CI 95%±0.018)至0.852(±0.033),灵敏度和特异性分别为0.739(CI 95%±0.047)至0.840(CI 95%± 0.064)和0.770(CI 95%±0.030)至0.865(CI 95%±0.038)。这些结果与以前的研究相似。但是,我们的方法允许持续更新和短期预测范围,这可能会提供主要优势。

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