Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study.

ABSTRACT:

Background

Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate.

Objective

We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes.

Methods

We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes

SUBMITTER: Ge W 

PROVIDER: S-EPMC9270709 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature