What is Machine Learning? A Primer for the Epidemiologist
What is Machine Learning? A Primer for the Epidemiologist
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DOI：
10.1093/aje/kwz189
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发表时间：
2019-12-01
影响因子：
5
通讯作者：
Lessler, Justin
中科院分区：
文献类型：
--
作者：
Bi, Qifang;Goodman, Katherine E.;Lessler, Justin
机器学习是计算机科学的一个分支，具有改变流行病学科学的潜力。随着人们对“大数据”的日益关注，它为流行病学家提供了新的工具来解决经典方法不太适合的问题。然而，为了批判性地评估将机器学习算法与现有方法相结合的价值，必须解决这两个领域之间的语言和技术障碍，这些障碍可能会使流行病学家难以阅读和评估机器学习研究。在这里，我们概述了机器学习文献中使用的概念和术语，其中包括一组不同的工具，目标从预测到分类再到聚类。我们简要介绍了5种常见的机器学习算法和4种基于集成的方法。然后，我们在已发表的文献中总结了机器学习技术的流行病学应用。我们推荐将机器学习整合到流行病学研究中的方法，并讨论整合机器学习和现有流行病学研究方法的机遇和挑战。
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.