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

Formative Evaluation of Non-Ehealth Innovations (154) AI Language Models in Health Care (90) Language Processing, Classifiers, and Syndrome Definitions (45) Ontologies, Classifications, and Coding (259) Research Instruments, Questionnaires, and Tools (640)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54803, first published .
Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study

Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study

Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study

Authors of this article:

Jiajia Li1, 2, 3 Author Orcid Image ;   Zikai Wang1, 2, 4 Author Orcid Image ;   Longxuan Yu5 Author Orcid Image ;   Hui Liu6 Author Orcid Image ;   Haitao Song1, 2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Jiajia Li   1, 2, 3 * , PhD ;   Zikai Wang   1, 2, 4 * , PhD ;   Longxuan Yu   5 , MSc ;   Hui Liu   6 , PhD ;   Haitao Song   1, 2 , PhD

1 Shanghai Artificial Intelligence Research Institute Co., Ltd, Shanghai, China

2 Xiangfu Laboratory, Jiaxing, China

3 School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, China

4 Inner Mongolia Academy of Science and Technology, Hohhot, China

5 University of California San Diego, San Diego, CA, United States

6 Shanghai Civil Aviation College, Shanghai, China

*these authors contributed equally

Corresponding Author:

  • Haitao Song, PhD
  • Shanghai Artificial Intelligence Research Institute Co., Ltd
  • No. 930, Jianchuan Road, Minhang District
  • Shanghai, 200240
  • China
  • Phone: 8618817771407
  • Email: songhaitao@sairi.com.cn