Department of Radiology, University of Calgary, Calgary, AB, Canada.
J Neural Eng. 2020 Nov 19;17(6). doi: 10.1088/1741-2552/abbff2.
In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.
在日益数据驱动的世界中，人工智能有望成为将大数据转化为实际效益的关键工具，医疗保健领域也不例外。机器学习旨在识别多维数据中的复杂模式，并利用这些未被发现的模式对新的未见案例进行分类或进行数据驱动的预测。近年来，深度神经网络在各种分类和回归任务中表现出的能力远远超过了传统的机器学习方法。在本文中，我们提供了一个通俗易懂的教程，介绍了最重要的监督机器学习概念和方法，包括深度学习，这些概念和方法对于医疗领域可能是最相关的。我们的目标是消除机器学习的一些神秘感，并说明机器学习模型如何对医疗应用有用。最后，本教程为如何针对通用医疗问题正确设计机器学习模型提供了一些实用建议。
