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

Natural Language Processing (706) Artificial Intelligence (1421) Electronic Health Records (1068)

Published on in Vol 7, No 3 (2019): Jul-Sep

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study

Authors of this article:

Fei Li1, 2, 3 Author Orcid Image ;   Yonghao Jin1 Author Orcid Image ;   Weisong Liu1, 2, 3 Author Orcid Image ;   Bhanu Pratap Singh Rawat4 Author Orcid Image ;   Pengshan Cai4 Author Orcid Image ;   Hong Yu1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (102) Tweetations (5) Metrics

Fei Li   1, 2, 3 , PhD ;   Yonghao Jin   1 , MSc ;   Weisong Liu   1, 2, 3 , PhD ;   Bhanu Pratap Singh Rawat   4 , MSc ;   Pengshan Cai   4 , MSc ;   Hong Yu   1, 2, 3, 4 , PhD

1 Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States

2 Center for Healthcare Organization and Implementation Research, Bedford Veterans Affairs Medical Center, Bedford, MA, United States

3 Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States

4 School of Computer Science, University of Massachusetts, Amherst, MA, United States

Corresponding Author:

  • Hong Yu, PhD
  • Department of Computer Science
  • University of Massachusetts Lowell
  • 1 University Avenue
  • Lowell, MA
  • United States
  • Phone: 1 978-934-6132
  • Email: Hong_Yu@uml.edu