Enhancing Smart Healthcare Services Through Deep Learning-Based Medical Data Analysis

In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity Recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care.