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.

翻译：本研究以MIMIC-IV数据库为数据源，探讨利用动态、高频、多元时间序列生命体征数据（包括体温、心率、平均血压、呼吸频率及血氧饱和度）中ICU住院前8小时的监测数据。通过比较多种聚类算法，最终选择了一种名为Time2Feat的端到端多元时间序列聚类系统，并结合K-Means算法，作为对ICU患者进行聚类的有效方法。在聚类分析中，使用2008年至2016年间收治的8080名患者数据用于模型开发，2017年至2019年间收治的2038名患者数据用于模型验证。通过分析不同类别间临床死亡预后的差异，发现不同亚组间的ICU死亡风险和住院死亡风险存在显著差异。此外，本研究还可视化了生命体征变化轨迹。该研究结果为多元时间序列聚类系统在ICU患者管理与监测中的潜在应用提供了有价值的见解。