Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection.

ABSTRACT:

Objective

Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes.

Materials and methods

Patients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in

SUBMITTER: Bhavani SV 

PROVIDER: S-EPMC10198539 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature