Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia.
School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia.
Intern Med J. 2021 Sep;51(9):1388-1400. doi: 10.1111/imj.15200.
Machine learning is a tool for analysing digitised data sets and formulating predictions that can optimise clinical decision-making. It aims to identify complex patterns in large data sets and encode them into models that can then classify new unseen cases or make predictions on new data. Machine learning methods take several forms and individual models can be of many different types. More than 50 models have been approved for use in routine healthcare, and the numbers continue to grow exponentially. The reliability and robustness of any model depends on multiple factors, including the quality and quantity of the data used to develop the models, and the selection of features in the data considered most important to maximising accuracy. In ensuring models are safe, effective and reproducible in routine care, physicians need to have some understanding of how these models are developed and evaluated, and to collaborate with data and computer scientists in their design and validation. This narrative review introduces principles, methods and examples of machine learning in a way that does not require mastery of highly complex statistical and computational concepts.
机器学习是一种分析数字化数据集并制定预测的工具，可以优化临床决策。它旨在识别大数据集中的复杂模式，并将其编码为模型，然后可以对新的未见病例进行分类或对新数据进行预测。机器学习方法有多种形式，并且单个模型可以属于许多不同的类型。已经有 50 多个模型被批准用于常规医疗保健，而且这个数字还在呈指数级增长。任何模型的可靠性和稳健性都取决于多个因素，包括用于开发模型的数据的质量和数量，以及选择对最大化准确性最重要的数据特征。为了确保模型在常规护理中安全、有效且可重复，医生需要了解这些模型是如何开发和评估的，并与数据科学家和计算机科学家合作设计和验证模型。本综述以不需要掌握高度复杂的统计和计算概念的方式介绍了机器学习的原理、方法和示例。
