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，探讨了ICU住院前8小时监测的动态、高频、多元时间序列生命体征数据（包括体温、心率、平均血压、呼吸频率和SpO2）的应用价值。通过比较多种聚类算法，最终选择基于K-Means结合的端到端多元时间序列聚类系统Time2Feat作为对ICU患者进行聚类的最有效方法。在聚类分析中，采用2008至2016年期间收治的8,080例患者数据进行模型开发，并使用2017至2019年期间收治的2,038例患者数据进行模型验证。通过分析不同类别患者的临床死亡率预后差异，发现不同亚组间存在不同的ICU死亡风险和住院死亡风险。此外，研究还可视化了生命体征的变化轨迹。本研究结果揭示了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价值。