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

Machine Learning (1501) Data Science (278) Natural Language Processing (706) Infodemiology and Infoveillance (1369) Mass Media/Social Media Communication and Campaigns (466)

Published on in Vol 10, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37201, first published .
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach

Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach

Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach

Authors of this article:

Adrian Ahne1, 2 Author Orcid Image ;   Vivek Khetan3 Author Orcid Image ;   Xavier Tannier4 Author Orcid Image ;   Md Imbesat Hassan Rizvi5 Author Orcid Image ;   Thomas Czernichow2 Author Orcid Image ;   Francisco Orchard2 Author Orcid Image ;   Charline Bour6 Author Orcid Image ;   Andrew Fano3 ;   Guy Fagherazzi6 Author Orcid Image
Article Authors Cited by (7) Tweetations (8) Metrics

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  6. Trevena W, Zhong X, Alvarado M, Semenov A, Oktay A, Devlin D, Gohil A, Chittimouju S. Utilizing Large Language Models to Detect and Understand Drug Discontinuation Events in Online Forums: Development and Validation Study (Preprint). Journal of Medical Internet Research 2023 View