  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.
