Validation of an alcohol misuse classifier in hospitalized patients.

Journal: Alcohol (Fayetteville, N.Y.)

Published Date: Sep 28, 2019

Abstract

BACKGROUND: Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health record (EHR) may further augment the identification of alcohol misuse. Natural language processing (NLP) with supervised machine learning has been successful at analyzing clinical notes and identifying cases of alcohol misuse in trauma patients.

Authors

Daniel To

Health Sciences Division, Burn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University, Maywood, Illinois, USA.
Brihat Sharma

Department of Computer Science, Loyola University Chicago, Chicago, IL, USA.
Niranjan Karnik

Department of Psychiatry, Rush University Medical Center, Chicago, Illinois, USA.
Cara Joyce

Loyola University Chicago, Chicago, IL.
Dmitriy Dligach

Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
Majid Afshar

Loyola University Chicago, Chicago, IL.

Keywords

Adult Alcoholism Case-Control Studies Electronic Health Records Female Humans Inpatients Male Middle Aged Natural Language Processing Reproducibility of Results Sensitivity and Specificity Supervised Machine Learning Tertiary Care Centers

External Resources

View on PubMed Access via DOI PubMed (31574300)

Validation of an alcohol misuse classifier in hospitalized patients.

Abstract

Authors

Keywords

External Resources

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