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Survival Tree01:19

Survival Tree

48
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...
48

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随机森林算法使用基本医疗数据来预测结肠多的存在.

Mihaela-Flavia Avram1,2, Nicolae Lupa3, Dimitrios Koukoulas4

  • 1Department of Surgery X, 1st Surgery Discipline, "Victor Babeș" University of Medicine and Pharmacy Timișoara, Timisoara, Romania.

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概括

这项研究开发了一种随机森林模型,使用患者数据和实验室测试来预测结直肠多. 该模型显示出良好的预测能力,有助于早期检测,可能降低结直肠癌风险.

关键词:
人工智能的人工智能是人工智能.预防结肠直肠癌的预防结肠直肠多体是一种多体.机器学习是机器学习.随机的森林随机的森林风险预测模型的风险预测模型

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科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 大肠直肠癌 (CRC) 往往先于大肠直肠多的恶性转变.
  • 早期检测和清除息肉可以显著降低CRC的死亡率和发病率.

研究的目的:

  • 使用机器学习开发结直肠多存在的预测模型.
  • 用基本的患者信息和常见的实验室测试结果来预测多.

主要方法:

  • 一个随机森林算法被训练在164名患者的数据,包括人口统计,病史和实验室结果.
  • 该模型在80%的数据上进行了内部验证,并在42名患者上进行了外部验证.
  • 性能与通用线性模型 (GLM) 和支持矢量机器 (SVM) 相比较.

主要成果:

  • 随机森林模型在测试组中达到0.820的曲线下的面积 (AUC),在外部验证中达到0.79.
  • 关键预测因素包括体重指数,血小板,血红蛋白,甘油三和转氨酶水平.
  • 随机森林在内部和外部验证中都超过了GLM和SVM.

结论:

  • 一个随机森林预测模型有效地预测了结肠多的存在.
  • 该模型利用人口统计数据,病史和常规血液检查进行准确的预测.
  • 这种工具为早期检测和预防CRC提供了显著的潜力.