Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Nov 1, 2019

Abstract

OBJECTIVE: Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks.

Authors

Dmitriy Dligach

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

Loyola University Chicago, Chicago, IL.
Timothy Miller

School of Computing and Information Systems, University of Melbourne, Victoria 3010, Australia.

Keywords

Algorithms Big Data Clinical Coding Datasets as Topic Deep Learning Electronic Health Records Humans Information Storage and Retrieval Natural Language Processing Neural Networks, Computer Substance-Related Disorders

External Resources

View on PubMed Access via DOI PubMed (31233140)

Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse.

Abstract

Authors

Keywords

External Resources

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