Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

ABSTRACT:

Background

Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs.

Methods

This study used a randomly selected cohort from the population-based Mayo Clinic Biobank (N = 300, age ≥65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium

SUBMITTER: Fu S 

PROVIDER: S-EPMC8893184 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature