论文标题
部分可观测时空混沌系统的无模型预测
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
论文作者
论文摘要
许多现代研究领域越来越依赖收集和分析大规模的，通常是非结构化和笨拙的数据集。因此，对机器学习和人工智能应用程序的兴趣越来越大，可以利用这种“数据洪水”。这个广泛的非技术概述为机器学习提供了温和的介绍，特别关注医学和生物应用。我们解释了可以解决的机器学习算法和典型任务的常见类型，以医疗保健的具体示例来说明基础知识。最后，我们对机器学习驱动药物的开放挑战，局限性和潜在影响提供前景。
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.
