Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning.

ABSTRACT:

Objective

To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.

Methods

This was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incre

SUBMITTER: Horng S 

PROVIDER: S-EPMC5383046 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature