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

Machine Learning (1501) Quality/Credibility of eHealth Information and Trust Issues (544) Natural Language Processing (706) Information Retrieval (296)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29175, first published .
Predicting Health Material Accessibility: Development of Machine Learning Algorithms

Predicting Health Material Accessibility: Development of Machine Learning Algorithms

Predicting Health Material Accessibility: Development of Machine Learning Algorithms

Authors of this article:

Meng Ji1 Author Orcid Image ;   Yanmeng Liu1 Author Orcid Image ;   Tianyong Hao2 Author Orcid Image
Article Authors Cited by (4) 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. Castellanos S, Figueroa C. Cognitive accessibility in health care institutions. Pilot study and instrument proposal. Data and Metadata 2023;2:22 View
  3. Nattam A, Vithala T, Wu T, Bindhu S, Bond G, Liu H, Thompson A, Wu D. Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations. Journal of Medical Internet Research 2023;25:e46346 View
  4. Chu L, Liu Y, Zhai Y, Wang D, Wu Y. The use of deep learning integrating image recognition in language analysis technology in secondary school education. Scientific Reports 2024;14(1) View