由药物的无监督潜伏特征学习驱动的集群分析,以确定重症患者的新型药物表型
Andrea Sikora1, Hayoung Jeong2, Mengyun Yu3
1Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, USA. sikora@uga.edu.
在无监督的聚类中,在重症监护病房 (ICU) 患者中确定了六种药物药理类型. 这些不同的群体在死亡率上表现出显著差异,突出了优化治疗决策的潜力.
科学领域:
- 关键护理医学 关键护理医学
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 重症监护室 (ICU) 的患者表现出显著的异质性.
- 目前的表型化方法缺乏全面的药物数据.
- 识别不同的患者亚组对于个性化治疗至关重要.
研究的目的:
- 通过无监督机器学习,在重症患者中探索独特的药物集群 (药物类型).
- 为了将已识别的药理类型与ICU并发症和以患者为中心的结果相关联.
- 调查药物数据在临重病患者的表型化中的有用性.
主要方法:
- 在991种药物配置上使用受限制的博尔兹曼机器进行无监督聚类.
- 分析药物类型,ICU并发症 (例如机械呼吸) 和结果 (例如停留时间,死亡率) 之间的相关性.
主要成果:
- 鉴定出了六种独特的药物表型.
- 药物类型表现出不同的药物概况和临床特征.
- 在药物类型之间观察到显著的死亡率差异 (例如,9.0%与21.9%,p < 0.0001).
- 药基类型4的死亡率明显高 (21.9%),比其他类型高 (2.5%9%).
结论:
- 基于机器学习的药物表型识别可以揭示ICU中临床相关的患者子组.
- 这种方法识别了与传统分类方法不明显的差异.
- 药理类型的识别可以提高临床决策和治疗优化危急病患者.
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