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

Theme issue 2019: Health Natural Language Processing (17) Electronic Health Records (1254) Machine Learning (1927) Natural Language Processing (855) Ontologies, Classifications, and Coding (303)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17652, first published .
Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

Authors of this article:

Xiaoyi Pan1 Author Orcid Image ;   Boyu Chen1 Author Orcid Image ;   Heng Weng2 Author Orcid Image ;   Yongyi Gong1 Author Orcid Image ;   Yingying Qu3 Author Orcid Image
Article Authors Cited by (5) Tweetations (1) Metrics

Journals

  1. Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Medical Informatics 2021;9(10):e23898 View
  2. Zhong X, Cambria E. Time expression recognition and normalization: a survey. Artificial Intelligence Review 2023 View
  3. Lyu S, Zhang C, Sun J, Weng H, Xue C, Guo X, Zhang A. Chinese herbal medicine for migraine management: A hospital-based retrospective analysis of electronic medical records. Frontiers in Medicine 2022;9 View
  4. Wang S, Li R, Wu H. Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts. Computer Methods and Programs in Biomedicine 2023;233:107474 View

Conference Proceedings

  1. Seegmiller P, Gatto J, Basak M, Cook D, Ghasemzadeh H, Stankovic J, Preum S. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints View