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 agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.

The odds of responding well to "intensifying" antidiabetic regimens with an additional antihyperglycemic and of avoiding episodes of severe hypoglycemia could be increased by promising approaches in big data, machine learning, and real-time informatics, according to recent presentations at the American Diabetes Association (ADA) 78th Scientific Sessions, Orlando, Florida. The decision to add a glucagon-like peptide-1 receptor agonist (GLP-1 RA) to basal insulin and other oral antihyperglycemic agents that have failed to adequately control a patient's type 2 diabetes (T2DM) could be better informed, for example, with analysis of a range of patient characteristics including the other medications and dosages, and the severity and duration of diabetic symptoms and of concurrent conditions. Big-data algorithms might be used to consider these multiple parameters, and to possibly identify optimal patient characteristics for the new drug therapy, according to Esther Zimmermann, PhD, Novo Nordisk, Søborg, Denmark. "Machine learning is a new tool used for the analysis of big data that has the potential to identify trends and predict outcomes," Zimmermann explained, in describing her study. "The aim of this study was to use machine learning for extensive analysis of big, complex to data to, one, characterize patients on basal insulin for whom a GLP-1 RA was additionally prescribed and, two, identify predictors of 1 percent (or greater) reduction in A1c in (those) patients."