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

Theme Issue 2020:National NLP Clinical Challenges/Open Health Natural Language Processing 2019 Challenge Selected Papers (17) Natural Language Processing (716) Electronic Health Records (1076) Decision Support for Health Professionals (1222) Clinical Information and Decision Making (1454) Clinical Informatics (1059)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23587, first published .
Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study

Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study

Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study

Authors of this article:

Kecheng Zhan1 Author Orcid Image ;   Weihua Peng2 Author Orcid Image ;   Ying Xiong1 Author Orcid Image ;   Huhao Fu1 Author Orcid Image ;   Qingcai Chen1, 3 Author Orcid Image ;   Xiaolong Wang1 Author Orcid Image ;   Buzhou Tang1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Nunes M, Bone J, Ferreira J, Elvas L. Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review. JMIR Medical Informatics 2024;12:e60164 View
  2. Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Wang Z. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal of the American Medical Informatics Association 2024 View