Hügle Maria, Omoumi Patrick, van Laar Jacob M, Boedecker Joschka, Hügle Thomas
Department of Computer Science, University of Freiburg, Freiburg, Germany.
Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, and University of Lausanne, Lausanne, Switzerland.
Rheumatol Adv Pract. 2020 Feb 19;4(1):rkaa005. doi: 10.1093/rap/rkaa005. eCollection 2020.
Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient's opinion and the rheumatologist's empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.
作为人工智能领域的一个分支，机器学习在医学中的应用越来越广泛，旨在辅助患者和医生。不断增长的数据集为应用机器学习方法提供了坚实的基础，这些方法能够从以往的经验中学习。本文综述解释了机器学习的基础知识及其监督学习、无监督学习、强化学习和深度学习等子领域。我们概述了机器学习目前在风湿病学中的应用，主要是用于电子诊断、疾病检测和医学图像分析的监督学习方法。未来，机器学习可能会帮助风湿病学家预测疾病进程并识别重要的疾病因素。更有趣的是，机器学习或许能够提出治疗建议并评估其预期益处（例如通过强化学习）。因此，在未来，共同决策不仅将包括患者的意见以及风湿病学家基于经验和证据的经验，还将受到机器学习证据的影响。
