Background:New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics.Methods and Results:This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics.Conclusions:We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

中文翻译：

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采用机器学习的临床风险预测模型比较分析的性能指标

背景：机器学习技术等新方法已越来越多地用于提高临床决策风险预测的性能。然而，通常报告的性能指标可能不足以捕捉这些新提出的模型的优势，以供医疗保健专业人员采用以改善护理。机器学习模型通常可以改进这些指标可能遗漏的某些子群体的风险估计。 方法和结果：本文解决了常见报告的性能比较指标的局限性，并提出了其他指标。我们的讨论涵盖与整体性能、鉴别、校准、分辨率、重新分类和模型实施相关的指标。