Transforming Healthcare: AI-NLP Fusion Framework for Precision Decision-Making and Personalized Care Optimization in the Era of IoMT - Rawas, Soha; Tafran, Cerine; AlSaeed, Duaa; Al-Ghreimil, Nadia (Computers, materials & continua, 2024)
In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT. Through the integration of advanced data analytics methodologies with NLP capabilities, we propose a comprehensive framework designed to enhance personalized medicine, streamline disease diagnosis, provide treatment recommendations, and optimize resource allocation. Using a systematic methodology data was collected from open data repositories, then preprocessed using data cleaning, missing value imputation, feature engineering, and data normalization and scaling. Optimization algorithms, such as Gradient Descent, Adam Optimization, and Stochastic Gradient Descent, were employed in the framework to enhance model performance. These were integrated with NLP processes, including Text Preprocessing, Tokenization, and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices. Lastly, through a synthesis of existing research and real-world case studies, we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency. The simulation produced compelling results, achieving an average diagnostic accuracy of 93.5% for the given scenarios, and excelled even further in instances involving rare diseases, achieving an accuracy rate of 98%. With regard to patient-specific treatment plans it generated them with an average precision of 96.7%. Improvements in early risk stratification and enhanced documentation were also noted. Furthermore, the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes, offering insights into risk-mitigating strategies. This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics, with implications for healthcare practitioners, researchers, and policymakers. By leveraging AI and NLP technologies in IoMT environments, this study paves the way for innovative strategies to enhance patient care and operational effectiveness. Ultimately, this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.