Evaluating prediction model performance.
Evaluating prediction model performance.
复制标题
评估预测模型的性能。
DOI：
10.1016/j.surg.2023.05.023
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发表时间：
2023
期刊：
影响因子：
3.8
通讯作者：
Ross,ElsieGyang
中科院分区：
文献类型：
--
作者：
Cabot,JohnH;Ross,ElsieGyang
本文强调了在使用临床数据评估为监督分类或回归任务开发的模型时要考虑的重要性能指标。在评估模型性能时，我们详细介绍了混淆矩阵、接收者工作特性曲线、F1分数、精确召回率曲线、均方误差和其他考虑因素的基础。在这个由先进预测模型快速扩散所定义的时代，熟悉接受者工作特征曲线下区域之外的各种性能指标以及在实施时评估模型值的细微差别对于确保有效的资源分配和最佳的患者护理提供至关重要。
This article highlights important performance metrics to consider when evaluating models developed for supervised classification or regression tasks using clinical data. When evaluating model performance, we detail the basics of confusion matrices, receiver operating characteristic curves, F1 scores, precision-recall curves, mean squared error, and other considerations. In this era, defined by the rapid proliferation of advanced prediction models, familiarity with various performance metrics beyond the area under the receiver operating characteristic curves and the nuances of evaluating model value upon implementation is essential to ensure effective resource allocation and optimal patient care delivery.