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
Article Authors Cited by (10) Tweetations (11) Metrics

Marie Humbert-Droz   1 , PhD ;   Pritam Mukherjee   1 , PhD ;   Olivier Gevaert   1, 2 , PhD

1 Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, United States

2 Department of Biomedical Data Science, Stanford University, Stanford, CA, United States

Corresponding Author:

  • Olivier Gevaert, PhD
  • Stanford Center for Biomedical Informatics Research
  • Department of Medicine
  • Stanford University
  • Medical School Office Building
  • Stanford, CA, 94305
  • United States
  • Phone: 1 650 721 2378
  • Email: olivier.gevaert@stanford.edu