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

Electronic Health Records (971) Machine Learning (1401) Clinical Information and Decision Making (1340)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32903, first published .
Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Authors of this article:

Marie Humbert-Droz1 Author Orcid Image ;   Pritam Mukherjee1 Author Orcid Image ;   Olivier Gevaert1, 2 Author Orcid Image
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