Sajda Paul
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Annu Rev Biomed Eng. 2006;8:537-65. doi: 10.1146/annurev.bioeng.8.061505.095802.
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.
机器学习为开发用于分析高维多模态生物医学数据的复杂、自动且客观的算法提供了一种有原则的方法。本综述聚焦于当前技术水平的若干进展，这些进展在改善疾病的检测、诊断和治疗监测方面已显示出前景。进展的关键在于对与算法构建和学习理论相关的关键问题有了更深入的理解和理论分析。这些问题包括在最大化泛化性能方面的权衡、使用符合物理实际的约束以及纳入先验知识和不确定性。本综述描述了机器学习的最新进展，重点关注监督和无监督线性方法以及贝叶斯推理，它们在生物医学疾病的检测和诊断中产生了重大影响。我们描述了不同的方法，并针对每种方法提供其在生物医学诊断特定领域应用的示例。
