机器学习衍生的血液培养分类,在重症监护病房内具有预测和预后值:一项回顾性队列研究
Jin Zhang1, Wanjun Liu1, Wenyan Xiao1
1The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui, China; Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui, China.
Intensive & critical care nursing
|October 7, 2023
概括
机器学习确定了重症监护病房 (ICU) 中患有血流感染的四个患者群,每个患者的预后和最佳抗生素持续时间都不同. 这种数据驱动的方法可以改善患者的护理.
科学领域:
- 关键护理医学 关键护理医学
- 传染性疾病 传染性疾病
- 在医疗保健中的数据科学.
背景情况:
- 在重症监护室 (ICU) 获得的血液感染 (BSI) 带来了重大管理挑战.
- 目前的诊断和管理策略通常依赖于阳性血液培养.
研究的目的:
- 开发一个数据驱动的分类模型,以优化ICU患者BSI的管理.
- 根据血液培养样本采集前的临床数据来表征患者群.
主要方法:
- 使用MIMIC-IV数据库 (n=2451) 的数据开发了一个无监督集群模型 (k-means),并在MIMIC-III (n=2047) 上进行了验证.
- 临床特征,预后,血液培养结果和抗微生物干预措施在已识别的集群中进行了比较.
主要成果:
- 在血液培养48小时前,确定了四个不同的患者群 (α,β,γ,δ).
- 群体γ表现出28天死亡率最高的群体,而群体γ和 δ的格兰氏阴性和真菌感染率更高.
- 在特定抗生素持续时间 (4,6天和7天) 中,分别在α,δ和γ集群中观察到最佳的28天死亡率.
结论:
- 机器学习成功地将患有BSI的ICU患者分为不同的预后组.
- 数据驱动的血液培养分类可以揭示潜在的异质性,指导优化治疗和改善患者的治疗结果.
- 需要进一步的研究来完善这些分类的临床应用.
关键词:
血液培养 血液培养是什么?导管 导管 导管 导管 导管分类 分类 分类 分类.集群分析就是对集群进行分析.重症监护室的重症监护室的重症监护室.机器学习是机器学习.预后 预后 预测 预测追溯研究是一项回顾性研究.更多相关视频
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