AI-Driven Social Media Listening And Automated Adverse Drug Reaction (ADR) Extraction System For Advanced Pharmacovigilance

Background: Traditional post-marketing drug monitoring framework metrics rely heavily upon passive, spontaneous clinical case notification configurations. These configurations systematically suffer from extensive reporting lag phases and a massive underreporting of actual consumer adverse drug reactions (ADRs). With the explosive expansion of global digital platforms, modern patient networks frequently document raw, unformatted clinical complaints online long before consulting a healthcare practitioner. Objective: This core research constructs, validates, and evaluates an enterprise cloud architecture prototype termed "Shreyas SmartPV Mobile" to automate case intake pipelines, deploy cognitive semantic extractors, map raw text onto official MedDRA hierarchies, and execute immediate regulatory eligibility checks. Methodology: A multi-layered low-code pipeline structure was engineered using Glide Apps and Softr for responsive frontend client interface extraction, Airtable as the primary secure relational cloud database storage, and Make.com as the background workflow synchronization integration orchestrator. Data streams are processed via a cognitive node driven by the Google Gemini Large Language Model (LLM) framework running specific clinical validation rules, multi-stage Named Entity Recognition (NER), and automated token dictionaries to extract suspect therapeutic drug classes cleanly from descriptive toxicological events. Results: Extensive experimental validation using standard chemical benchmark metrics (including Paracetamol, Aspirin, and Ibuprofen scenarios) verified 100% precision in parsing unstructured digital narratives, mapping baseline complaints to standard MedDRA Preferred Terms (PT) (e.g., matching 'red rashes on skin' to Erythematous Rash), and computing immediate validation states (Report Valid? = True) based on strict international safety compliance requirements. The system recorded an average automated triage execution velocity of less than 3.9 seconds per array. Conclusion: The validated automation workflow confirms that cloud-native cognitive processing pipelines cut manual data entry fatigue, optimize processing costs, and maximize patient-reported signal capture. This technical transition shifts drug safety management from passive tracking arrays into dynamic, real-time digital active safety listening ecosystems.