AI-Integrated Medical Drug Store For Symptom Analysis and Intelligent Drug Recommendation

Abstract

The rapid growth of medical data and the increasing complexity of clinical diagnostics requires effective computational frameworks to help people in healthcare industry. This paper presents an integrated intelligent healthcare system which is aimed at improving disease prediction and personalized drug recommendations. Based on the recent advancements in Artificial Intelligence, the proposed system uses hybrid deep learning architecture. First, it makes use of Transformer-based Natural Language Processing (NLP) models to analyze clinical symptoms and Electronic Health Records (EHR), which greatly increases diagnostic accuracy compared to traditional decision-tree methods. Second, the system combines the Graph Neural Networks (GNNs) with an ontology driven databases like DrugBank and UMLS to provide safe and effective drug recommendations. This module specifically models drug to drug interactions and checks for safety using OpenFDA label data, which ensures reliable treatment planning. By merging symptom-based disease inference with the reinforcement learning based personalization, this approach fills some of the significant gaps in automated healthcare. It offers scalable solution for precision medicine and better resource allocation.