Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.

ABSTRACT:

Background

Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning.

Methods

A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan. 1 and June 30, 2015, were randomly selected, and we compared the performance of EMR phenotype algorithms developed using machine learning with an algorithm based on the Canadian version of the International

SUBMITTER: Martin EA 

PROVIDER: S-EPMC9933992 | biostudies-literature | 2023 Jan-Feb

REPOSITORIES: biostudies-literature