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

Peer-to-Peer Support and Online Communities (637) Information Seeking, Information Needs (467) Machine Learning (1471) Natural Language Processing (693) Hypertension Prevention and Treatment (224)

Published on in Vol 22, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17349, first published .
Multidimensional Feature Classification of the Health Information Needs of Patients With Hypertension in an Online Health Community Through Analysis of 1000 Patient Question Records: Observational Study

Multidimensional Feature Classification of the Health Information Needs of Patients With Hypertension in an Online Health Community Through Analysis of 1000 Patient Question Records: Observational Study

Multidimensional Feature Classification of the Health Information Needs of Patients With Hypertension in an Online Health Community Through Analysis of 1000 Patient Question Records: Observational Study

Authors of this article:

Aijing Luo1, 2 Author Orcid Image ;   Zirui Xin1, 2, 3 Author Orcid Image ;   Yifeng Yuan1, 2, 3 Author Orcid Image ;   Tingxiao Wen3 Author Orcid Image ;   Wenzhao Xie1, 2 Author Orcid Image ;   Zhuqing Zhong1, 2 Author Orcid Image ;   Xiaoqing Peng1, 2 Author Orcid Image ;   Wei Ouyang1, 2, 3 Author Orcid Image ;   Chao Hu2, 4, 5 Author Orcid Image ;   Fei Liu1, 2, 3 Author Orcid Image ;   Yang Chen1, 2, 3 Author Orcid Image ;   Haiyan He1, 2, 3 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Yang Y, Hou M, Gong X, Guo R, Feng X, Tian R. Quality Assessment of Hypertension Treatment–Related Information on WeChat: Cross-sectional Study. Journal of Medical Internet Research 2022;24(10):e38567 View
  2. Wang J, Wang X, Wang L, Peng Y. Health Information Needs of Young Chinese People Based on an Online Health Community: Topic and Statistical Analysis. JMIR Medical Informatics 2021;9(11):e30356 View
  3. Liu J, Liu Y. Exploring the User Interaction Network in an Anxiety Disorder Online Community: An Exponential Random Graph Model with Topical and Emotional Effects. International Journal of Environmental Research and Public Health 2022;19(11):6354 View
  4. Liu J, Liu Y. Motivation Research on the Content Creation Behaviour of Young Adults in Anxiety Disorder Online Communities. International Journal of Environmental Research and Public Health 2021;18(17):9187 View
  5. Qi M, Cui J, Li X, Han Y. Influence of E-consultation on the Intention of First-Visit Patients to Select Medical Services: Results of a Scenario Survey. Journal of Medical Internet Research 2023;25:e40993 View
  6. Cheng Q, Lin Y. Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network. JMIR Formative Research 2023;7:e42297 View
  7. Gu D, Liu H, Zhao H, Yang X, Li M, Liang C. A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demand. Journal of the Association for Information Science and Technology 2024;75(2):152 View
  8. Nie L, Xu J, Wang R, Tilga H. Health information needs and feedback of users in the online TCM community. PLOS ONE 2024;19(3):e0301536 View
  9. Gou Y, Li R, Zhuang Z. China’s spatial and temporal position in the global research network of information behavior and its current development dynamics. Library Hi Tech 2024 View