使用监督式机器学习的分类与预测模型：概念性综述
Classification and predictive models using supervised machine learning: A conceptual review.
作者信息
Pienaar M A, Naidoo K D
机构信息
Department of Paediatrics and Child Health, Division Critical Care, Faculty of Health Sciences, School of Clinical Medicine, University of the Free State, Bloemfontein, South Africa.
Paediatric Critical Care Unit, Universitas Academic Hospital, Bloemfontein, South Africa.
出版信息
South Afr J Crit Care. 2025 May 19;41(1):e2937. doi: 10.7196/SAJCC.2025.v411.2937. eCollection 2025.
BACKGROUND
Supervised machine learning models (SMLMs) are likely to be a prevalent approach in the literature on medical machine learning. These models have considerable potential to improve clinical decision-making through enhanced prediction and classification. In this review, we present an overview of SMLMs. We provide a discussion of the conceptual domains relevant to machine learning, model development, validation, and model explanation. This discussion is accompanied by clinical examples to illustrate key concepts.
CONTRIBUTION OF THE STUDY
This conceptual review provides an overview and guide to the interpretation of SMLMs in the medical literature.
背景
监督式机器学习模型（SMLMs）很可能是医学机器学习文献中的一种普遍方法。这些模型通过增强预测和分类，在改善临床决策方面具有相当大的潜力。在本综述中，我们对监督式机器学习模型进行概述。我们讨论与机器学习、模型开发、验证及模型解释相关的概念领域。此次讨论伴有临床实例以阐释关键概念。
研究贡献
本概念性综述对医学文献中监督式机器学习模型的解释进行概述并提供指导。
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