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

JMIR Theme Issue: COVID-19 Special Issue (2452) Natural Language Processing (693) Machine Learning (1465) Knowledge Translation and Implementation Science (398) Information Retrieval (287) Epublishing and Open Access (86) Theme Issue: Medical Informatics and COVID-19 (107) Infectious Diseases (non-STD/STI) (1575)

Published on in Vol 23, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22860, first published .
Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study

Authors of this article:

Zhao-Hua Lu1 Author Orcid Image ;   Jade Xiaoqing Wang1 Author Orcid Image ;   Xintong Li2 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

Journals

  1. Gupta P, Garg R, Kaur A. Question Answering Systems for Covid-19. Journal of Physics: Conference Series 2021;2062(1):012027 View
  2. Pourreza M, Ensan F. Towards semantic-driven boolean query formalization for biomedical systematic literature reviews. International Journal of Medical Informatics 2023;170:104928 View
  3. Khader A, Ensan F. Learning to rank query expansion terms for COVID-19 scholarly search. Journal of Biomedical Informatics 2023;142:104386 View
  4. Huang G, Li Y, Jameel S, Long Y, Papanastasiou G. From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?. Computational and Structural Biotechnology Journal 2024;24:362 View