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

Theme Issue 2021: Health Natural Language Processing and Applications (16) Electronic Health Records (1068) Natural Language Processing (706) Machine Learning (1501) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Clinical Informatics (1050) Stroke (166) Secondary Use of Clinical Data for Research and Surveillance (375) Ontologies, Classifications, and Coding (243)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29120, first published .
Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Authors of this article:

Bruna Stella Zanotto1, 2 Author Orcid Image ;   Ana Paula Beck da Silva Etges1, 3 Author Orcid Image ;   Avner dal Bosco3 Author Orcid Image ;   Eduardo Gabriel Cortes4 Author Orcid Image ;   Renata Ruschel1 Author Orcid Image ;   Ana Claudia De Souza5 Author Orcid Image ;   Claudio M V Andrade6 Author Orcid Image ;   Felipe Viegas6 Author Orcid Image ;   Sergio Canuto6 Author Orcid Image ;   Washington Luiz6 Author Orcid Image ;   Sheila Ouriques Martins5 Author Orcid Image ;   Renata Vieira7 Author Orcid Image ;   Carisi Polanczyk1, 2 Author Orcid Image ;   Marcos André Gonçalves6 Author Orcid Image
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