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

Machine Learning (1501) Quality/Credibility of eHealth Information and Trust Issues (544) Information Retrieval (296) Learning and Education (77) Design and Evaluation of Medical Education Materials (32) Evaluation of Medical Education (81) Linguistics, Comprehension, and Learning (53)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25110, first published .
Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study

Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study

Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study

Authors of this article:

Wenxiu Xie1 Author Orcid Image ;   Christine Ji2 Author Orcid Image ;   Tianyong Hao3 Author Orcid Image ;   Chi-Yin Chow1 Author Orcid Image
Article Authors Cited by (2) Tweetations (5) Metrics

Journals

  1. Ayre J, Bonner C, Muscat D, Dunn A, Harrison E, Dalmazzo J, Mouwad D, Aslani P, Shepherd H, McCaffery K. Multiple Automated Health Literacy Assessments of Written Health Information: Development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor v1. JMIR Formative Research 2023;7:e40645 View
  2. Jiang Y, Zhao Q, Li A, Wu Z, Liu L, Lin F, Li Y. Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis. Clinical and Applied Thrombosis/Hemostasis 2024;30 View