Expecting the unexpected, and prioritizing the predictable.
作者信息
Wayda Brian, Khush Kiran K
机构信息
Department of Medicine, Division of Cardiology, Stanford University School of Medicine, Stanford, California.
Department of Medicine, Division of Cardiology, Stanford University School of Medicine, Stanford, California.
出版信息
J Heart Lung Transplant. 2022 Aug;41(8):1128-1129. doi: 10.1016/j.healun.2022.04.005. Epub 2022 Apr 22.
Abstract
摘要
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Expecting the unexpected, and prioritizing the predictable.预料意外之事，并优先处理可预测之事。
J Heart Lung Transplant. 2022 Aug;41(8):1128-1129. doi: 10.1016/j.healun.2022.04.005. Epub 2022 Apr 22.
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Is it too soon to abandon all hope of useful post-transplant predictions in thoracic transplantation? Reply to: Expecting the unexpected, and prioritizing the predictable.现在就放弃胸段移植术后有用预测的所有希望是否为时过早？回复：期待意外，并优先考虑可预测的情况。
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本文引用的文献
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Assessing the accuracy of the lung allocation score.评估肺分配评分的准确性。
J Heart Lung Transplant. 2022 Feb;41(2):217-225. doi: 10.1016/j.healun.2021.10.015. Epub 2021 Oct 28.
2
Using machine learning to improve survival prediction after heart transplantation.运用机器学习改善心脏移植术后的生存预测。
J Card Surg. 2021 Nov;36(11):4113-4120. doi: 10.1111/jocs.15917. Epub 2021 Aug 19.
3
Great variability in donor heart acceptance practices across the United States.美国各地在供体心脏接受标准方面存在巨大差异。
Am J Transplant. 2020 Jun;20(6):1582-1596. doi: 10.1111/ajt.15760. Epub 2020 Jan 20.
4
Risk prediction models for survival after heart transplantation: A systematic review.心脏移植术后生存风险预测模型：一项系统综述。
Am J Transplant. 2020 Apr;20(4):1137-1151. doi: 10.1111/ajt.15708. Epub 2019 Dec 16.
5
Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.机器学习技术的预测能力可能受到数据集特征的限制：来自 UNOS 数据库的见解。
J Card Fail. 2019 Jun;25(6):479-483. doi: 10.1016/j.cardfail.2019.01.018. Epub 2019 Feb 6.
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