This paper is in the following e-collection/theme issue:

Quality Improvement (260) Natural Language Processing (706)

Published on in Vol 6, No 1 (2018): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9150, first published .
Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

Authors of this article:

Jennifer Hornung Garvin1, 2, 3, 4, 5 Author Orcid Image ;   Youngjun Kim2, 6 Author Orcid Image ;   Glenn Temple Gobbel7, 8 Author Orcid Image ;   Michael E Matheny7, 8 Author Orcid Image ;   Andrew Redd2, 4 Author Orcid Image ;   Bruce E Bray2, 3 Author Orcid Image ;   Paul Heidenreich9 Author Orcid Image ;   Dan Bolton2, 4 Author Orcid Image ;   Julia Heavirland2 Author Orcid Image ;   Natalie Kelly2 Author Orcid Image ;   Ruth Reeves7, 8 Author Orcid Image ;   Megha Kalsy2, 3 Author Orcid Image ;   Mary Kane Goldstein10, 11 Author Orcid Image ;   Stephane M Meystre2, 6 Author Orcid Image
Article Authors Cited by (28) Tweetations (1) Metrics

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  17. Houssein E, Mohamed R, Ali A. Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review. IEEE Access 2021;9:140628 View
  18. Manlhiot C, van den Eynde J, Kutty S, Ross H. A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology. Canadian Journal of Cardiology 2022;38(2):169 View
  19. Anetta K, Horak A, Wojakowski W, Wita K, Jadczyk T. Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases. Journal of Personalized Medicine 2022;12(6):869 View
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Books/Policy Documents

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