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对于住院COVID-19肺炎患者的无监督机器学习集群方法
Nuttinan Nalinthasnai1, Ratchainant Thammasudjarit2, Tanapat Tassaneyasin1,3
1Division of Pulmonary and Pulmonary Critical Care Medicine, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Rama VI Road, Ratchathewi, Bangkok, 10400, Thailand.
BMC pulmonary medicine
|February 8, 2025
概括
机器学习确定了三个COVID-19肺炎患者子组. 一个严重的子组面临更高的死亡率和输管风险,指导个性化治疗以获得更好的结果.
科学领域:
- 医疗信息学医学信息学
- 计算生物学是一种计算生物学.
- 临床研究是临床研究.
背景情况:
- 个性化治疗依赖于识别不同的疾病临床表型.
- 将住院COVID-19肺炎患者分为子组对量身定制的护理至关重要.
- 无监督机器学习为发现这些患者子组提供了一种方法.
研究的目的:
- 将住院的COVID-19肺炎患者分为不同的临床子组.
- 使用无监督机器学习方法进行表型识别.
- 为了在已识别的子组中比较临床结果.
主要方法:
- 对住院的COVID-19肺炎患者进行了K-means集群.
- 在入院后24小时内获得的临床和实验室数据用于集群.
- 考克斯的比例危险模型评估了输管风险和90天死亡率.
主要成果:
- 在538名患者中发现了三种不同的临床表型.
- 集群3,以年龄较大和严重标志物为特征,显示死亡率和输管风险明显较高.
- 集群1的生存率为100%,而集群2代表了较不严重的群体.
结论:
- 确定了三种不同的COVID-19肺炎临床表型.
- 集群3与呼吸衰竭和死亡风险增加有关.
- 这些发现支持针对COVID-19肺炎患者的量身定制的临床管理策略.
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