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改进可解释的机器学习应急部门分类工具,解决阶级不平衡问题.

Clarisse Sj Look1, Salinelat Teixayavong1, Therese Djärv2

  • 1Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.

Digital health
|May 6, 2024
PubMed
概括

在紧急风险预测分数 (SERP) 中解决阶级不平衡改善了其表现. 新的SERP+模型通过更好地分层患者风险来提高急诊室分拣准确度.

关键词:
机器学习 机器学习紧急情况部门的急救部门.可以解释的选分类是为了分类.

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科学领域:

  • 医疗保健中的机器学习
  • 临床选和风险预测
  • 紧急医疗分析 紧急医疗分析

背景情况:

  • 紧急风险预测分数 (SERP) 是一种机器学习工具,旨在帮助紧急部门 (ED) 的分拣决策.
  • 最初的SERP模型显示了短期死亡率的良好预测性能,但是在一个具有显著类失衡的数据集上开发的.
  • 医学数据集中的类失衡可能会损害机器学习模型的预测准确性.

研究的目的:

  • 调查在SERP开发过程中解决类不平衡是否可以提高其预测性能.
  • 确定改进的SERP模型 (SERP+) 是否可以导致ED中更准确的分类决策.
  • 将修改后的SERP+分数与原始SERP和其他标准分类风险分数的性能进行比较.

主要方法:

  • 利用了来自新加坡总医院ED的大量数据集 (2008-2020年1,833,908条记录).
  • 在AutoScore-Imbalance框架内采用随机过量采样和不足采样技术来开发SERP+分数.
  • 进行了预测性能 (AUC,灵敏度,特异性等) 的比较. 在单独的测试组中,SERP+与SERP和常见分拣分数的比较.

主要成果:

  • 开发的SERP+分数包括五到六个变量,与原始SERP分数 (0.859-0.894) 相比,AUC值 (0.874-0.905) 较高.
  • 对于SERP+-7d和SERP+-30d,观察到统计学意义,这表明在解决类不平衡后,预测能力更强.
  • SERP+在灵敏度,特异性,平衡准确度和积极预测值方面显示出微不足道的改善,表现优于常见的分类风险得分.

结论:

  • 在培训阶段解决阶级不平衡问题显著改善了SERP风险预测得分的表现.
  • 增强的SERP+模型提供了更好的患者风险分层,这对于有效的ED分类至关重要.
  • 像SERP+这样的基于机器学习的分数具有很大的潜力,可以支持紧急部门的准确,数据驱动的分拣决策.