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

Consumer & Patient Education and Shared-Decision Making (679) Peer-to-Peer Support and Online Communities (636) Information Retrieval (287) Diabetes Education and Elearning for Patients (114) Diabetes Self-Management (484)

Published on in Vol 19, No 10 (2017): October

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches

Authors of this article:

Yuqun Zeng1, 2, 3 Author Orcid Image ;   Xusheng Liu1 Author Orcid Image ;   Yanshan Wang2 Author Orcid Image ;   Feichen Shen2 Author Orcid Image ;   Sijia Liu2, 4 Author Orcid Image ;   Majid Rastegar Mojarad2 Author Orcid Image ;   Liwei Wang2 Author Orcid Image ;   Hongfang Liu2 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Wang Z, Huang H, Cui L, Chen J, An J, Duan H, Ge H, Deng N. Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System. JMIR Medical Informatics 2020;8(4):e17642 View
  2. Pugatch J, Grenen E, Surla S, Schwarz M, Cole-Lewis H. Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review. Journal of Medical Internet Research 2018;20(3):e97 View
  3. Jing X. The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis. JMIR Medical Informatics 2021;9(8):e20675 View
  4. Liu X, Zhou Y, Wang Z. Preference access of users' cancer risk perception using disease-specific online medical inquiry texts. Information Processing & Management 2022;59(1):102737 View
  5. Liu X, Zhou Y, Wang Z, Kumar A, Biswas B. Disease Topic Modeling of Users' Inquiry Texts: A Text Mining-Based PQDR-LDA Model for Analyzing the Online Medical Records. IEEE Transactions on Engineering Management 2024;71:6319 View
  6. Tang B, Hu J, Wang X, Chen Q, Hao T. Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM‐CRF. Wireless Communications and Mobile Computing 2018;2018(1) View