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

Information Seeking, Information Needs (426) Quality/Credibility of eHealth Information and Trust Issues (492) Machine Learning (1400) Natural Language Processing (650) Information Quality in Digital Media (318) Information Retrieval (271) Information Demand (31) Infodemic Management (44)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26065, first published .
Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Authors of this article:

Aleksandra Nabożny1 Author Orcid Image ;   Bartłomiej Balcerzak2 Author Orcid Image ;   Adam Wierzbicki2 Author Orcid Image ;   Mikołaj Morzy3 Author Orcid Image ;   Małgorzata Chlabicz4 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Aleksandra Nabożny   1 , MSc ;   Bartłomiej Balcerzak   2 , PhD ;   Adam Wierzbicki   2 , Prof Dr Hab ;   Mikołaj Morzy   3 , PhD, DSc ;   Małgorzata Chlabicz   4 , MD, PhD

1 Department of Software Engineering, Gdańsk University of Technology, Gdańsk, Poland

2 Polish-Japanese Academy of Information Technology, Warsaw, Poland

3 Faculty of Computing and Telecommunications, Poznan University of Technology, Poznań, Poland

4 Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland

Corresponding Author: