Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records.

ABSTRACT:

Objective

To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.

Materials and methods

Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

Results

A diverse cohort of 11 313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and seve

SUBMITTER: Ta CN 

PROVIDER: S-EPMC9620768 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature