(untitled) (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2024)
Medical data retrieval is becoming increasingly crucial, aiding physicians and domain experts in more effectively accessing knowledge and information related to medicine and facilitating informed decision-making. The centralized architectures need help with scalability and real-time indexing, leading to extended retrieval times and decreased efficiency. To address these issues, a novel MEdical Data retrieval using Inception resNET (MEDI-NET) has been proposed to retrieve medical data efficiently. The proposed system introduces a deep learning network for a dynamic poly-indexing model and concurrent indexing, ensuring the real-time retrieval of the latest medical records. EMRBots and MIMIC-III are the two datasets used to compare the performance of the proposed MEDI-NET approach with existing MRCG, HCAC-EHR, and FedCBMIR method, which is implemented using Python. The effectiveness of the proposed MEDI-NET approach has been determined using evaluation metrics such as precision, accuracy, recall, F1-score, indexing time, and retrieval time. Comparative analysis with existing methods demonstrates superior precision, accuracy, recall, and F1 score for the proposed method. Additionally, the proposed system exhibits reduced indexing and retrieval times, showcasing its efficiency in handling large-scale medical data. The proposed MEDI-NET approach’s accuracy is 0.60 %, 9.87 %, and 21.1 % higher than the existing MRCG, HCAC-EHR, and FedCBMIR techniques, respectively.