Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art that have shown promise in improving detection, diagnosis, and therapeutic monitoring of disease. Key in the advancement has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review describes recent developments in machine learning, focusing on supervised and unsupervised linear methods and Bayesian inference, which have made significant impacts in the detection and diagnosis of disease in biomedicine. We describe the different methodologies and, for each, provide examples of their application to specific domains in biomedical diagnostics.

中文翻译：

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机器学习用于疾病的检测和诊断。

机器学习为开发用于分析高维和多模式生物医学数据的复杂，自动和客观算法提供了一种原则方法。这篇综述着重于现有技术的一些进展，这些进展显示出有望改善疾病的检测，诊断和治疗监测。进展的关键是对与算法构建和学习理论有关的关键问题的更深入的理解和理论分析。这些措施包括为最大程度地提高泛化性能而进行的权衡，使用物理上现实的约束以及结合先验知识和不确定性。这篇评论描述了机器学习的最新发展，重点是有监督和无监督的线性方法以及贝叶斯推理，这对生物医学疾病的检测和诊断产生了重大影响。我们描述了不同的方法，并针对每种方法提供了将其应用于生物医学诊断中特定领域的示例。