TY - GEN
T1 - Utilizing AI and Social Media Analytics to Discover Unreported Adverse Side Effects of GLP-1 Receptor Agonists Used for Obesity Treatment
AU - Bartal, Alon
AU - Jagodnik, Kathleen M.
AU - Pliskin, Nava
AU - Seidmann, Abraham
N1 - Publisher Copyright: © 2025 IEEE Computer Society. All rights reserved.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonist (GLP-1 RA) medications used to treat diabetes and obesity, a market expected to grow exponentially to $133.5 billion USD by 2030. Using a named entity recognition model, our method successfully detected 15 potential ASEs of GLP-1 RAs, overlooked upon FDA approval. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed medications, leveraging cutting-edge AI-driven social media analytics. This ongoing research can increase the safety of new medications in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.
AB - Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonist (GLP-1 RA) medications used to treat diabetes and obesity, a market expected to grow exponentially to $133.5 billion USD by 2030. Using a named entity recognition model, our method successfully detected 15 potential ASEs of GLP-1 RAs, overlooked upon FDA approval. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed medications, leveraging cutting-edge AI-driven social media analytics. This ongoing research can increase the safety of new medications in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.
KW - Adverse Side Effect (ASE)
KW - Artificial Intelligence (AI)
KW - Glucagon-Like Peptide 1 Receptor Agonist (GLP-1 RA)
KW - Social Media Analytics
UR - http://www.scopus.com/inward/record.url?scp=105005138901&partnerID=8YFLogxK
U2 - 10.24251/hicss.2025.710
DO - 10.24251/hicss.2025.710
M3 - منشور من مؤتمر
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 5931
EP - 5940
BT - Proceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 58th Hawaii International Conference on System Sciences, HICSS 2025
Y2 - 7 January 2025 through 10 January 2025
ER -
