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Survival Tree01:19

Survival Tree

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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...
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Updated: Jul 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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使用螺旋式方法的系统审查与机器学习.

Amirhossein Saeidmehr1, Piers David Gareth Steel2, Faramarz F Samavati3

  • 1Computer Science Department, University of Calgary, 2500 University Dr., Calgary, Canada. amir.saeidmehr@cpsc.ucalgary.ca.

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|January 17, 2024
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概括
此摘要是机器生成的。

一种新的螺旋式机器学习方法显著提高了系统审查选效率. 这种方法提高了处理速度和准确性,使文献评论在不断增长的学术数据中更容易管理.

关键词:
积极学习是指积极学习.机器学习 机器学习系统性审查 系统性审查技术辅助审查技术辅助审查

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科学领域:

  • 信息科学 信息科学 信息科学
  • 计算机科学 计算机科学
  • 图书统计学 图书统计学

背景情况:

  • 学术文献的指数增长需要有效的系统审查方法.
  • 系统性审查中的当前机器学习应用程序通常遵循传统的以人为中心的工作流程 (例如PRISMA),从而限制了其潜力.
  • 优化机器学习集成对于管理越来越多的研究出版物来说至关重要.

研究的目的:

  • 为了评估一种新的螺旋,用于机器学习辅助系统审查选的交替方法.
  • 将螺旋式方法与各种机器学习配置中的传统顺序方法进行比较.
  • 确定螺旋式方法在提高系统审查的效率和可管理性方面的有效性.

主要方法:

  • 在360个不同的条件下,对三个数据集进行了模拟.
  • 测试的变量包括算法分类器,特征提取技术 (例如TF-IDF),优先级规则 (例如最大概率) 和数据类型.
  • 螺旋式方法涉及间歇性全文选,并与标题/摘要选相交.

主要成果:

  • 螺旋处理方法,特别是逻辑回归,TF-IDF矢量化和最大概率优先级,始终优于传统方法.
  • 观察到高达90%的显著改善,特别是在有较少符合条件文章的数据集中.
  • 这种优化的机器学习策略提高了系统审查中选组件的可行性.

结论:

  • 螺旋式机器学习方法比系统审查选的传统方法有很大的进步.
  • 这种方法预计将使系统性审查的选部分在未来10-20年内保持可实现.
  • 进一步的研究可以完善这些机器学习技术,以便在科学文献分析中得到更广泛的应用.