Making sense of abbreviations in nursing notes: A case study on mortality prediction.

• Type of Material: Article
• Authors: Jasmine Y. Nakayama, Emory University; Vicki Hertzberg, Emory University; Joyce C. Ho, Emory University
• Language: English
• Date: 2019
• Publisher: American Medical Informatics Association
• Publication Version: Final Published Version
• Copyright Statement: ©2019 AMIA - All rights reserved.
• Title of Journal or Parent Work: AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science
• ISSN: 2153-4063
• Volume: 2019
• Start Page: 275
• End Page: 284
• Grant/Funding Information: This work was supported by the National Institute of Health award 1K01LM012924-01 and the Robert Wood Johnson Foundation’s Future of Nursing Scholars program

Abstract

Unstructured data from electronic health records hold potential for improving predictive models for health outcomes. Efforts to extract structured information from the unstructured data used text mining methodologies, such as topic modeling and sentiment analysis. However, such methods do not account for abbreviations. Nursing notes have valuable information about nurses' assessments and interventions, and the abbreviation use is common. Thus, abbreviation disambiguation may add more insight when using unstructured text for predictive modeling. We present a new process to extract structured information from nursing notes through abbreviation normalization, lemmatization, and stop word removal. Our study found that abbreviation disambiguation in nursing notes for subsequent topic modeling and sentiment analysis improved prediction of in-hospital and 30-day mortality while controlling for comorbidity.