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相关概念视频

Survival Tree01:19

Survival Tree

388
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...
388

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随机森林机器学习与创伤严重程度评分中的人类专家准确度相匹配

G L Laing1, J L Bruce1, W Bekker1

  • 1Department of Surgery, University of KwaZulu-Natal, Durban, South Africa.

World journal of surgery
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测了创伤登记册中缺失的缩写伤害量表 (AIS) 和伤害严重程度得分 (ISS) 数据. 这提高了数据的完整性和准确性,这对于创伤护理和研究至关重要.

关键词:
缩写伤害量表缩写伤害量表电子医疗记录 电子医疗记录伤害严重程度得分是多少机器学习是机器学习.自然语言处理自然语言处理.随机的森林随机的森林创伤评分创伤的评分.

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科学领域:

  • 创伤护理和研究
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 准确的缩写伤害量表 (AIS) 和伤害严重程度评分 (ISS) 对创伤护理和研究至关重要.
  • 在创伤登记册中手动评分通常会导致由于遗漏而导致不完整的数据.
  • 混合电子医疗登记 (HEMR) 被一级创伤服务用于记录AIS和ISS.

研究的目的:

  • 评估机器学习算法在预测缺失的AIS和ISS分数方面的性能.
  • 评估ML驱动数据归算对创伤登记册完整性的影响.
  • 为了确定ML预测是否保持与人类得分可比的临床准确性.

主要方法:

  • 分析了来自HEMR的21,704名创伤患者记录.
  • 应用四个机器学习 (ML) 算法来预测每个身体区域缺失的AIS分数.
  • 从预测的AIS分数和使用R2,RMSE,MAE,灵敏度,特异性和Cohen的kappa的性能评估中数学推导ISS.

主要成果:

  • 随机森林模型表现出高准确度,R2=0.847,MAE=1.87,Cohen的kappa=0.893. 这两种模型都具有很高的准确度.
  • 高严重病例的敏感度为87.1%,低严重病例的特异性为100.0%.
  • 创伤登记数据的完整性从75.3%显著提高到88.3%,恢复了2815个缺失的分数.

结论:

  • 随机森林ML算法可靠地预测缺失的AIS和ISS分数.
  • 使用ML显著提高创伤登记数据的完整性.
  • ML预测实现了与人类专家评分相当的临床准确性,这对于创伤研究和护理至关重要.