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相关概念视频

Survival Tree01:19

Survival Tree

79
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
79

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相关实验视频

Updated: Jun 21, 2025

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
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SurvdigitizeR:一种用于自动生存曲线数字化的算法.

Jasper Zhongyuan Zhang1,2, Juan David Rios1, Tilemanchos Pechlivanoglou3

  • 1Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.

BMC medical research methodology
|July 13, 2024
PubMed
概括

卡普兰-梅尔曲线的自动数字化有效提取生存概率. 这种新的算法提供了与手工方法相比的准确性,在研究中节省了时间和资源.

关键词:
自动数字化自动化数字化卡普兰-梅耶尔曲线进行元分析分析.在 R 套餐中,闪亮的应用程序闪亮的应用程序生存分析的分析.

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科学领域:

  • 生物统计学 生物统计学
  • 医疗信息学 医疗信息学

背景情况:

  • 卡普兰-梅尔 (KM) 曲线对于医学研究中的生存分析至关重要.
  • 用于数据提取的KM曲线的手动数字化是劳动密集型的,容易出现错误.

研究的目的:

  • 开发和验证一个自动化算法,从KM曲线中提取生存概率.
  • 提高从已公布的生存数据中提取数据的效率和准确性.

主要方法:

  • 该算法使用颜色空间转换和光学字符识别处理图像文件 (JPG,PNG).
  • K-medoids 聚类用于区分图中重叠曲线.
  • 使用模拟数据和现实世界公布的KM曲线,与根平均平方误差 (RMSE) 和布兰德-阿尔特曼分析对手工数字化进行了性能验证.

主要成果:

  • 自动数字化在从模拟的KM曲线中提取生存概率方面表现出高准确性,平均RMSE为0.012.
  • 由于每个图形的曲线数量较多,以及存在审查标记,算法的准确性略有降低.
  • 现实世界的验证显示了自动化和手动数字化方法之间的强烈一致.

结论:

  • 开发的算法自动化KM曲线数字化,需要最小的用户输入,并提供与手工方法相比的准确性.
  • 该工具简化了用于决策分析模型和元分析的数据提取.
  • 该算法以开源R包和GitHub上的Shiny应用程序的形式提供.