Objective

This work aims to provide a review of the existing literature in the field of automated machine learning (AutoML) to help healthcare professionals better utilize machine learning models “off-the-shelf” with limited data science expertise. We also identify the potential opportunities and barriers to using AutoML in healthcare, as well as existing applications of AutoML in healthcare.

Methods

Published papers, accompanied with code, describing work in the field of AutoML from both a computer science perspective or a biomedical informatics perspective were reviewed. We also provide a short summary of a series of AutoML challenges hosted by ChaLearn.

Results

A review of 101 papers in the field of AutoML revealed that these automated techniques can match or improve upon expert human performance in certain machine learning tasks, often in a shorter amount of time. The main limitation of AutoML at this point is the ability to get these systems to work efficiently on a large scale, i.e. beyond small- and medium-size retrospective datasets.

Discussion

The utilization of machine learning techniques has the demonstrated potential to improve health outcomes, cut healthcare costs, and advance clinical research. However, most hospitals are not currently deploying machine learning solutions. One reason for this is that health care professionals often lack the machine learning expertise that is necessary to build a successful model, deploy it in production, and integrate it with the clinical workflow. In order to make machine learning techniques easier to apply and to reduce the demand for human experts, automated machine learning (AutoML) has emerged as a growing field that seeks to automatically select, compose, and parametrize machine learning models, so as to achieve optimal performance on a given task and/or dataset.

Conclusion

While there have already been some use cases of AutoML in the healthcare field, more work needs to be done in order for there to be widespread adoption of AutoML in healthcare.

中文翻译：

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自动化机器学习：回顾医疗保健的最新技术和机会。

目标

这项工作旨在回顾自动化机器学习 (AutoML) 领域的现有文献，以帮助医疗保健专业人员更好地利用数据科学专业知识有限的“现成”机器学习模型。我们还确定了在医疗保健中使用 AutoML 的潜在机会和障碍，以及 AutoML 在医疗保健中的现有应用。

方法

从计算机科学的角度或生物医学信息学的角度对已发表的论文以及代码进行了描述，这些论文描述了 AutoML 领域的工作。我们还提供了 ChaLearn 主持的一系列 AutoML 挑战的简短摘要。

结果

对 AutoML 领域的 101 篇论文的回顾表明，这些自动化技术可以在某些机器学习任务中匹配或提高人类专家的表现，通常是在更短的时间内。AutoML 在这一点上的主要限制是让这些系统在大规模上高效工作的能力，即超越中小型回顾性数据集。

讨论

机器学习技术的使用具有改善健康结果、降低医疗保健成本和推进临床研究的潜力。但是，大多数医院目前并未部署机器学习解决方案。造成这种情况的一个原因是，医疗保健专业人员通常缺乏构建成功模型、将其部署到生产中并将其与临床工作流程集成所必需的机器学习专业知识。为了使机器学习技术更易于应用并减少对人类专家的需求，自动化机器学习 (AutoML) 已成为一个不断发展的领域，旨在自动选择、组合和参数化机器学习模型，从而实现最优给定任务和/或数据集的性能。

结论

虽然在医疗保健领域已经有一些 AutoML 的用例，但需要做更多的工作才能在医疗保健领域广泛采用 AutoML。