致编辑的信：评估用于预测创伤中大量输血的机器学习方法 - Suppr

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Letter to the Editor: Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.

作者信息

Valiente Fernández Marcos

机构信息

Hospital Universitario 12 de Octubre, Avenida de Cordoba s/n, 28041, Madrid, Spain.

出版信息

World J Surg. 2023 Dec;47(12):3445-3446. doi: 10.1007/s00268-023-07138-7. Epub 2023 Aug 1.

DOI:10.1007/s00268-023-07138-7

PMID:37528273

Abstract

摘要

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本文引用的文献

Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.评估机器学习方法在创伤性大量输血预测中的应用。

World J Surg. 2023 Oct;47(10):2340-2346. doi: 10.1007/s00268-023-07098-y. Epub 2023 Jun 30.

Principles and Practice of Explainable Machine Learning.可解释机器学习原理与实践

Front Big Data. 2021 Jul 1;4:688969. doi: 10.3389/fdata.2021.688969. eCollection 2021.

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.在不平衡数据集上评估二元分类器时，精确率-召回率曲线比ROC曲线更具信息性。

PLoS One. 2015 Mar 4;10(3):e0118432. doi: 10.1371/journal.pone.0118432. eCollection 2015.

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