A Personalized Context-Aware Recommendation System using Natural Language Processing for Drug Supply Chain Management in Smart City Environments - Rao, Gali Nageswara; shree, Madhavarao Divya; Elaiyaraja, Arunachalam; Periaswamy, Bhanupriya; Prabhakar, Mattaparthi Bala; Sandeep Gupta (International Journal of Medical Toxicology and Forensic Medicine, 2026)
Background: Personalized Context-Aware Drug Recommendation Systems leverage machine learning (ML) techniques and patient data to provide tailored medication suggestions based on medical history, demographic information, and individual health profiles. By analyzing electronic health records, real-time physiological data, and genomic information, the system provides timely recommendations. This manuscript presents a personalized context-aware drug recommendation system using the optimal deep learning (PCADRS-ODL) method. The proposed technique aims to assist medical professionals in making effective treatment decisions. Methods: Initially, the input data undergoes preprocessing. Then, a BERT-based word embedding approach is employed for feature extraction. Next, the PCADRS-ODL system utilizes a Double Attention-Convolutional Neural Network with a Bidirectional Gated Recurrent Unit framework. Furthermore, the parameters of the DAC-BiGRU model are optimized using the Gannet Optimization Algorithm. Results: Experimental results on a benchmark dataset demonstrate that the proposed method achieves superior performance over existing methods. Conclusion: Overall, the proposed PCADRS-ODL method provides an accurate and effective personalized drug recommendation framework, demonstrating its potential to support clinical decision-making and improve patient-specific treatment outcomes.