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Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

机译：密集护理单位拔管失效预测机器学习模型的开发与验证

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Qin-Yu Zhao; Huan Wang; Jing-Chao Luo; Ming-Hao Luo; Le-Ping Liu; Shen-Ji Yu; Kai Liu; Yi-Jie Zhang; Peng Sun; Guo-Wei Tu; Zhe Luo;
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1. Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units [J] . Qin-Yu Zhao, Guo-Wei Tu, Zhe Luo, Frontiers in Medicine . 2021,第a期

机译：强化护理单位拔管失效预测机器学习模型的开发与验证
2. Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units [J] . Meng Hsuen Hsieh, Meng Ju Hsieh, Chin-Ming Chen, Scientific reports. . 2018,第1期

机译：比较机器学习模型预测重症监护病房计划外拔管患者的死亡率
3. Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit [J] . Muhammad A Rishi, Rahul Kashyap, Gregory Wilson, BMC Anesthesiology . 2014,第1期

机译：回顾性推导和验证算法，以识别重症监护室中的拔管失败
4. Development and validation of a system based on spectral-photometry for measuring fluid dynamics of multi-infusion conditions in Intensive Care units [C] . Annemoon Timmerman, Brechtje Riphagen, John Klaessens, Design and quality for biomedical technologies II . 2009

机译：基于分光光度法的重症监护病房多次输液状况流体动力学测量系统的开发和验证
5. Lung-thorax compliance measured during a spontaneous breathing trial is a good index of extubation failure in the surgical intensive care unit: a retrospective cohort study [D] . 岡部悠吾 2019

机译：自发呼吸试验中测得的肺胸顺应性是外科重症监护室拔管失败的良好指标：一项回顾性队列研究
6. Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units [O] . Qin-Yu Zhao, Huan Wang, Jing-Chao Luo, 2021

机译：密集护理单位拔管失效预测机器学习模型的开发与验证
7. Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit [O] . Muhammad Adeel Rishi, Rahul Kashyap, Gregory Wilson, 2014

机译：回顾性推导和验证算法，以识别重症监护室中的拔管失败

Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

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