Open AccessActive neural networks to detect mentions of changes to medication treatment in social media
Author(s) -
Davy Weissenbacher,
Suyu Ge,
Ari Z Klein,
Karen O’Connor,
Robert Gross,
Sean Hennessy,
Graciela GonzalezHernandez
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab158
Subject(s) - social media , medicine , convolutional neural network , medication adherence , population , artificial intelligence , computer science , world wide web , environmental health
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.