Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit.

ABSTRACT:

Objective

Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers.

Methods

This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts' eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to th

SUBMITTER: Jeddah D 

PROVIDER: S-EPMC8369051 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature