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相关概念视频

Introduction to R01:11

Introduction to R

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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相关实验视频

Updated: Jul 1, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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使用R和RStudio进行EHR实验室数据预处理和季节调整的协议.

Victorine P Muse1, Søren Brunak2

  • 1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.

STAR protocols
|March 1, 2024
PubMed
概括

实验室数据中的季节性可能会扭曲诊断. 该协议使用R软件来分析电子健康记录的季节性模式,并调整临床参考间隔,提高诊断准确度.

关键词:
生物信息学是一种生物信息学.健康科学 卫生科学 卫生科学系统生物学 系统生物学

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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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相关实验视频

Last Updated: Jul 1, 2025

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科学领域:

  • 临床实验室科学 临床实验室科学
  • 生物统计学 生物统计学
  • 医疗信息学 医疗信息学

背景情况:

  • 实验室医疗保健数据显示季节性变化,可能导致患者诊断不足或过度.
  • 准确的诊断参考间隔对于有效的临床决策至关重要.

研究的目的:

  • 提出一种可复制的协议,用于分析实验室医疗保健数据中的季节性.
  • 根据已识别的季节性模式,提供调整现有参考区间的方法.
  • 利用电子健康记录 (EHR) 数据进行可靠的季节性分析.

主要方法:

  • 预处理全人口患者实验室数据到统一的数据集.
  • 对季节性分析的相关层次的定义.
  • 用R软件将数据规范化到中位数,并适应选定的统计函数.

主要成果:

  • 该协议允许在实验室测试结果中识别和量化季节性趋势.
  • 详细说明了调整参考间隔以考虑季节性的方法.
  • 这种方法有助于在一年中更准确地解释实验室数据.

结论:

  • 分析实验室数据的季节性对于减轻诊断错误至关重要.
  • 这种基于R的协议提供了一种标准化的方法来调整参考间隔,提高诊断精度.
  • 实施这些调整可以通过确保更准确的诊断来改善患者护理.