{"title":"使用 R 的自动机器学习:面向临床研究初学者的 AutoML 工具。","authors":"Youngho Park","doi":"10.7602/jmis.2024.27.3.129","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73832,"journal":{"name":"Journal of minimally invasive surgery","volume":"27 3","pages":"129-137"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416892/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automated machine learning with R: AutoML tools for beginners in clinical research.\",\"authors\":\"Youngho Park\",\"doi\":\"10.7602/jmis.2024.27.3.129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":73832,\"journal\":{\"name\":\"Journal of minimally invasive surgery\",\"volume\":\"27 3\",\"pages\":\"129-137\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416892/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of minimally invasive surgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7602/jmis.2024.27.3.129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of minimally invasive surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7602/jmis.2024.27.3.129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
最近,随着应用领域的大幅扩展,人们对机器学习(ML)的兴趣与日俱增。虽然 ML 方法在许多领域都很出色,但建立 ML 管道需要大量时间和人力资源。自动化 ML(AutoML)工具提供了一种解决方案,它能将数据预处理、模型选择、超参数优化和预测分析等重复性任务自动化。本综述介绍了 AutoML 工具在一般研究(包括临床研究)中的应用。特别是,它概述了一种使用 R 编程语言(R 统计计算基础)的简单方法,初学者也可以使用。此外,还提供了二元分类的实用代码和输出结果,以方便临床研究人员在未来的研究中直接应用。
Automated machine learning with R: AutoML tools for beginners in clinical research.
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.