This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were compared, and an end-to-end multivariate time series clustering system called Time2Feat, combined with K-Means, was chosen as the most effective method to cluster patients in the ICU. In clustering analysis, data of 8,080 patients admitted between 2008 and 2016 was used for model development and 2,038 patients admitted between 2017 and 2019 for model validation. By analyzing the differences in clinical mortality prognosis among different categories, varying risks of ICU mortality and hospital mortality were found between different subgroups. Furthermore, the study visualized the trajectory of vital signs changes. The findings of this study provide valuable insights into the potential use of multivariate time-series clustering systems in patient management and monitoring in the ICU setting.

本研究使用MIMIC-IV数据库作为数据来源，研究了包括温度、心率、平均血压、呼吸率和SpO2等动态的、高频的、多变量的时间序列生命体征数据，并比较了各种聚类算法，选择了一种名为Time2Feat的端到端多变量时间序列聚类系统与K-Means相结合的方法作为最有效的聚类方法，对2008年至2016年间入住的8,080名患者的数据进行了模型开发和对2017年至2019年间入住的2,038名患者的模型验证。通过分析不同类别之间的临床死亡预后差异，发现不同亚组之间的ICU死亡和住院死亡的风险不同。此外，该研究可视化了生命体征变化的轨迹。本研究的结果为多变量时间序列聚类系统在ICU中患者管理和监测中的潜在应用提供了有价值的见解。

基于真实世界生命体征数据的端到端多元时间序列聚类算法识别ICU患者亚组