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.

翻译：经FDA批准后揭示的药物不良副作用（ASEs）对患者安全构成威胁。为及时检测被忽视的ASEs，我们开发了一种数字健康方法，能够分析来自社交媒体、已发表的临床研究、制造商报告和ChatGPT的海量公共数据。我们发现了与胰高血糖素样肽-1受体激动剂（GLP-1 RA）相关的ASEs——该类药物的市场规模预计将呈指数级增长，到2030年达到1335亿美元。通过使用命名实体识别（NER）模型，我们的方法成功检测到21种在FDA批准时被忽视的潜在ASEs，包括易怒和麻木。我们的数据分析方法利用基于AI的尖端社交媒体分析技术，彻底革新了对新上市药物未报告ASEs的检测能力。通过释放社交媒体的力量，支持监管机构和制造商快速发现隐藏的ASE风险，可提升市场上新药的安全性。