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Updated: Jun 6, 2025

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使用基于机器学习的决策树分析预测内血管大动脉修复后的短期死亡率.

Toshiya Nishibe1, Tsuyoshi Iwasa2, Masaki Kano3

  • 1Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan; Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan.

Annals of vascular surgery
|November 23, 2024
PubMed
概括

基于机器学习的决策树分析 (DTA) 可以预测内血管动脉瘤修复 (EVAR) 后的短期死亡率. 营养状况不佳,病,慢性肺炎和晚年是EVAR结果的关键预测因素.

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相关概念视频

Survival Tree01:19

Survival Tree

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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