医学可解释性的系统地图

Hajar Hakkoum, Ibtissam Abnane, A. Idri
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引用次数: 1

摘要

机器学习(ML)一直在迅速发展,主要是由于历史数据集的可用性和先进的计算能力。这种增长仍然面临着一系列挑战,比如ML模型的可解释性。特别是在医学领域,可解释性是医生使用机器学习的真正瓶颈。这篇综述是根据众所周知的系统地图过程进行的,从不同方面分析可解释性技术在医学领域的应用。从6个数字图书馆共选取179篇文献(1994-2020)。研究结果表明,近年来,处理可解释性的研究数量有所增加,但以解决方案和基于实验的实证类型为主。此外,人工神经网络是研究可解释性的最广泛使用的ML黑盒技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Systematic Map of Interpretability in Medicine
Machine learning (ML) has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. This review was carried out according to the well-known systematic map process to analyse the literature on interpretability techniques when applied in the medical field with regard to different aspects. A total of 179 articles (1994-2020) were selected from six digital libraries. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Additionally, artificial neural networks were the most widely used ML black-box techniques investigated for interpretability.
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