Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Jan 15, 2021

Abstract

OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied natural language processing (NLP) to analyze indications towards or against LTFU in HIV-positive patients' notes.

Authors

Tomasz Oliwa

The University of Chicago, Chicago, IL.
Brian Furner

Pediatrics, University of Chicago, Chicago, IL, USA.
Jessica Schmitt

Department of Medicine, University of Chicago, Chicago, Illinois, USA.
John Schneider

Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Illinois, USA.
Jessica P Ridgway

Department of Medicine, University of Chicago, Chicago, Illinois, USA.

Keywords

Adult Electronic Health Records Female HIV Infections Humans Lost to Follow-Up Male Models, Theoretical Natural Language Processing Patient Compliance Retention in Care

External Resources

View on PubMed Access via DOI PubMed (33150369)

Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

Abstract

Authors

Keywords

External Resources

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