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

Generative Language Models Including ChatGPT (320) Data Science (276) Natural Language Processing (704) AI Language Models in Health Care (59) Artificial Intelligence (1407) Machine Learning (1488) Chatbots and Conversational Agents (542)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54580, first published .
An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study

An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study

An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study

Authors of this article:

Lei Wang1, 2 Author Orcid Image ;   Yinyao Ma3 Author Orcid Image ;   Wenshuai Bi1 Author Orcid Image ;   Hanlin Lv1 Author Orcid Image ;   Yuxiang Li1, 2 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Lei Wang   1, 2 * , MS ;   Yinyao Ma   3 * , MS, MD ;   Wenshuai Bi   1 , MS ;   Hanlin Lv   1 , MD, PhD ;   Yuxiang Li   1, 2 , PhD

1 BGI Research, Wuhan, China

2 Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen, China

3 Department of Obstetrics, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China

*these authors contributed equally

Corresponding Author:

  • Hanlin Lv, MD, PhD
  • BGI Research
  • 1-2F, Building 2, Wuhan Optics Valley International Biomedical Enterprise Accelerator Phase 3.1
  • No 388 Gaoxin Road 2, Donghu New Technology Development Zone
  • Wuhan, 430074
  • China
  • Phone: 86 18707190886
  • Email: lvhanlin@genomics.cn