Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.

ABSTRACT:

Background

Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.

Methods

CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients' chart data were linked to administrative discharge abstract database (DAD) and Sunrise^™ Clinical Manager (SCM) EMR database rec

SUBMITTER: Pan J 

PROVIDER: S-EPMC10474977 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature