Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.
Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.
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揭秘结构化表格数据分析中的统计和机器学习。
DOI：
10.1016/j.arth.2023.08.045
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Taunton,MichaelJ
中科院分区：
文献类型：
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作者：
Khosravi,Bardia;Weston,AlexanderD;Nugen,Fred;Mickley,JohnP;MaraditKremers,Hilal;Wyles,CodyC;Carter,RickeyE;Taunton,MichaelJ
电子健康记录促进了对具有许多变量的大量数据的提取和分析，用于临床护理和研究。传统的基于回归的统计方法可能无法捕获高维数据分析中的所有复杂性。因此，研究人员越来越多地使用基于机器学习（ML）的方法来更好地处理这些更具挑战性的数据集，以发现患者数据中的隐藏模式，并用于分类和预测目的。本文介绍了结构化数据分析中常用的ML方法，并以骨科手术为例。我们提出了在启动ML项目和评估该领域已发表的研究时的实际考虑。
Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients’ data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.