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在接受全关节置换的73,000名糖尿病患者中识别风险组:机器学习集群分析.

Alishah Ahmadi1, Anthony J Kaywood1, Alejandra Chavarria1

  • 1School of Medicine, New York Medical College, Valhalla, NY 10595, USA.

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PubMed
概括
此摘要是机器生成的。

机器学习确定了6组糖尿病患者接受关节置换手术. 与感染相关的并发症显著增加了这些糖尿病患者非例行出院和更长时间住院的风险.

关键词:
集群集成是指集群集成.在糖尿病中,糖尿病是血糖性糖尿病.机器学习是机器学习.非例行性排放的排放.整体关节整形术

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科学领域:

  • 整形外科手术 整形外科手术
  • 数据科学数据科学数据科学
  • 公共卫生 公共卫生

背景情况:

  • 糖尿病 (DM) 是常见的,并影响关节整形术 (THA) 的结果.
  • 在糖尿病THA患者中确定特定的并发症概况对于改善护理至关重要.

研究的目的:

  • 应用机器学习聚类来定义糖尿病THA患者的并发症概况.
  • 评估确定集群与术后结果 (如出院准备和停留时间) 之间的关联.

主要方法:

  • 利用了2015-2021年国家住院患者样本数据库.
  • 包括73,606名接受THA治疗的糖尿病患者,分析了49种并发症和共变量.
  • 采用集群算法,用于非例行出院 (NRD) 的后勤回归,以及用于停留时间 (LOS) 的Kruskal-Wallis H测试.

主要成果:

  • 根据并发症概况,确定了六个不同的患者群.
  • 一个以尿路感染和败血症为特征的群体显示了显著更高的NRD风险 (OR 7.83) 和最长的中位数LOS (9.0天).
  • 其他群体表现出不同的恢复模式,一些群体实现了更短的 LOS (2.0 天).

结论:

  • 机器学习有效地将糖尿病THA患者分为六个独特的组.
  • 感染占主导地位的群体代表了对不良结果的高风险人群.
  • 这种聚类方法为糖尿病THA患者的风险分层和个性化术后管理提供了一种新方法.