Active neural networks to detect mentions of changes to medication treatment in social media. (6th October 2021)

• Record Type: Journal Article
• Authors: Weissenbacher, Davy; Ge, Suyu; Klein, Ari; O'Connor, Karen; Gross, Robert; Hennessy, Sean; Gonzalez-Hernandez, Graciela
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 12(2021)
• Page Start: 2551
• Page End: 2561
• Publication Date: 2021-10-06
• Subjects: social media -- pharmacovigilance -- medication non-adherence -- active learning -- text classification; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab158
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: We address a first step toward using social media data to supplement current efforts in monitoring population-level medication nonadherence: detecting changes to medication treatment. Medication treatment changes, like changes to dosage or to frequency of intake, that are not overseen by physicians are, by that, nonadherence to medication. Despite the consequences, including worsening health conditions or death, 50% of patients are estimated to not take medications as indicated. Current methods to identify nonadherence have major limitations. Direct observation may be intrusive or expensive, and indirect observation through patient surveys relies heavily on patients' memory and candor. Using social media data in these studies may address these limitations. Methods: We annotated 9830 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended by a physician. We used active and transfer learning from 12 972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus. To validate our CNN and explore future directions, we annotated 1956 positive tweets as to whether they reflect nonadherence and categorized the reasons given. Results: Our CNN achieved 0.50 F1 -score on this new corpus. The manual analysis of positive tweets revealed that nonadherence is evident in a subset with 9 categories of reasons for nonadherence. Conclusion: We showed that social media users publicly discuss medication treatment changes and may explain their reasons including when it constitutes nonadherence. This approach may be useful to supplement current efforts in adherence monitoring.