Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.
Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.
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DOI：
10.3390/ijms22094394
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发表时间：
2021-04-22
影响因子：
5.6
通讯作者：
Pérez-Sánchez H
中科院分区：
文献类型：
--
作者：
Banegas-Luna AJ;Peña-García J;Iftene A;Guadagni F;Ferroni P;Scarpato N;Zanzotto FM;Bueno-Crespo A;Pérez-Sánchez H
人工智能正在提供令人惊讶的结果，医学是它最喜欢的游乐场之一。机器学习，特别是深度神经网络是这场革命的幕后推手。医学领域最具挑战性的目标之一是癌症诊断和治疗，但要开始这场革命，软件工具需要适应新的要求。从这个意义上说，学习工具正在成为一种商品，但是，为了能够在日常生活中帮助医生，必须充分理解如何解释模型。在这项调查中，我们分析了当前的机器学习模型和其他应用于医学的计算机工具，特别是癌症研究，我们讨论了它们的可解释性，性能和输入数据。人工神经网络（ANN），逻辑回归（LR）和支持向量机（SVM）已被观察到是首选的模型。此外，在图形处理单元（GPU）和高性能计算（HPC）基础设施的快速发展的支持下，卷积神经网络（CNN）在图像处理可行时变得越来越重要。然而，机器学习预测的可解释性，以便医生能够理解它们，信任它们并为临床实践获得有用的见解，仍然很少被考虑，这是一个需要改进的因素，以提高医生的预测能力，并在不久的将来实现个性化治疗。
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.
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影响因子：
4.6
作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
通讯作者：
Lundin J
影响因子：
4.6
作者：
Dan Nguyen;Long, Troy;Jiang, Steve
通讯作者：
Jiang, Steve
影响因子：
3.1
作者：
Albertazzi, E;Cajone, F;Sherbet, GV
通讯作者：
Sherbet, GV
影响因子：
7.4
作者：
Chen, Songjing;Wu, Sizhu
通讯作者：
Wu, Sizhu
影响因子：
6.2
作者：
Ayer, Turgay;Alagoz, Oguzhan;Chhatwal, Jagpreet;Shavlik, Jude W.;Kahn, Charles E., Jr.;Burnside, Elizabeth S.
通讯作者：
Burnside, Elizabeth S.