Suppr超能文献

使用R进行自动化机器学习:面向临床研究初学者的自动机器学习工具。

Automated machine learning with R: AutoML tools for beginners in clinical research.

作者信息

Park Youngho

机构信息

Department of Big Data Application, College of Smart Interdisciplinary Engineering, Hannam University, Daejeon, Korea.

出版信息

J Minim Invasive Surg. 2024 Sep 15;27(3):129-137. doi: 10.7602/jmis.2024.27.3.129.

Abstract

Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools offer a solution by automating repetitive tasks, such as data preprocessing, model selection, hyperparameter optimization, and prediction analysis. This review introduces the use of AutoML tools for general research, including clinical studies. In particular, it outlines a simple approach that is accessible to beginners using the R programming language (R Foundation for Statistical Computing). In addition, the practical code and output results for binary classification are provided to facilitate direct application by clinical researchers in future studies.

摘要

近年来,随着机器学习(ML)应用领域的显著扩展,人们对它的兴趣与日俱增。尽管ML方法在许多领域表现出色,但建立一个ML流程需要大量的时间和人力资源。自动化机器学习(AutoML)工具通过自动化诸如数据预处理、模型选择、超参数优化和预测分析等重复性任务提供了一种解决方案。本综述介绍了AutoML工具在包括临床研究在内的一般研究中的应用。特别是,它概述了一种使用R编程语言(R统计计算基金会)的、初学者也能掌握的简单方法。此外,还提供了二元分类的实际代码和输出结果,以便临床研究人员在未来的研究中直接应用。

相似文献

1
Automated machine learning with R: AutoML tools for beginners in clinical research.
J Minim Invasive Surg. 2024 Sep 15;27(3):129-137. doi: 10.7602/jmis.2024.27.3.129.
2
Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML.
J Big Data. 2022;9(1):96. doi: 10.1186/s40537-022-00646-8. Epub 2022 Jul 20.
4
Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy.
Ophthalmol Sci. 2024 Jan 19;4(5):100470. doi: 10.1016/j.xops.2024.100470. eCollection 2024 Sep-Oct.
7
Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance.
Comput Struct Biotechnol J. 2024 Feb 9;25:9-19. doi: 10.1016/j.csbj.2024.02.003. eCollection 2024 Dec.
9
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.
Artif Intell Med. 2020 Apr;104:101822. doi: 10.1016/j.artmed.2020.101822. Epub 2020 Feb 21.

引用本文的文献

1
Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.
Drug Des Devel Ther. 2025 Jan 13;19:239-250. doi: 10.2147/DDDT.S495555. eCollection 2025.

本文引用的文献

1
3
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.
Artif Intell Med. 2020 Apr;104:101822. doi: 10.1016/j.artmed.2020.101822. Epub 2020 Feb 21.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验