Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study.

ABSTRACT:

Background

Delirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead to dementia, longer hospital stays, increased health costs, and death. Although delirium can be prevented and treated, it is difficult to identify and predict.

Objective

This study aimed to improve machine learning models that retrospectively identify the presence of delirium during hospital stays (eg, to measure the effectiveness of delirium prevention interventions) by using the natural language processing (NLP) technique of sentiment analysis (in this case a feature that identifies sentiment toward, or away from, a delirium diagnosis).

Methods

Using data from the General Medicine Inpatient Initiative, a Canadian hospital data and analy

SUBMITTER: Wang L 

PROVIDER: S-EPMC9812273 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature