Real-Time Healthcare Data Analytics across Hospitals and Pharmaceutical Networks Using Cognitive IoT

Abstract

The Research presents a new framework that is based on Federated Cognitive Edge Learning (FCEL) and developed using PyTorch networks to improve real-time medical data processing and cooperation. The suggested system allows decentralized analytics of hospitals and pharmaceutical networks, keeping the data confidential and being highly computing efficient. Through the combination of cognitive intelligence and federated learning, the structure can deliver dynamic contextual insights without the transmission of sensitive patient information. The experimental tests showed 94.7 prediction accuracy, 52 latency reduction, and total data confidentiality between the distributed nodes. The FCEL model, which is built with the help of PyTorch, allows closing the divide between the healthcare provider and pharmaceutical systems by means of safe, scalable, and smart data integration. Such strategy can greatly enhance patient monitoring, predict drug-response faster, and enable timely medical decisions, which can form a solid base of next generation real-time cognitive IoT healthcare ecosystems.