Data-Driven Decision Support System in Intelligent HealthCare

Machine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models. Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field. This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.

Debnath Bhattacharyya is a Professor in the Computer Science and Engineering Department, KL University, Bowrampet, Hyderabad, India. His research interests include Security Engineering, Pattern Recognition, Biometric Authentication, Multimodal Biometric Authentication, Data Mining and Image Processing. Yu-Chen Hu is a Professor in the Department of Computer Science at Tunghai University, Taichung City, Taiwan. His interests include image and signal processing, data compression, information hiding, information security, computer network, deep learning, and data engineering.

Preface. List of Contributors. Foundations of Computational Techniques in Healthcare and Drug Discovery: A Deep Learning Perspective. Machine Learning Algorithms and Models for Predictive Healthcare Analytics in Drug Discovery. Computational Intelligence Transforming Healthcare 4.0: Innovations in Medical Image Analysis through AI and IoT Integration. Unlocking Medical Data Intelligence: Methodologies and Practical Applications. Revolutionizing Health Informatics: Artificial Intelligence Applications in Health Care. Empowering Smart Healthcare with Federated Learning: Advancements in Human Health. Precision Prognosis in Oncology: Harnessing Deep Learning for Solid Tumor Imaging. Optimizing Healthcare Decision-Making: Advanced Models for Diverse Applications. Artificial Intelligence-Powered Disease Diagnosis: A New Era in Medical Practice. Cutting-Edge Medical Diagnostics: Identifying Cancerous and Non-Cancerous Tumors with Precision. Harnessing Deep Neural Networks for Human Disease Identification: Insights and Applications. Estimating Disease Severity with Precision: Leveraging Deep Neural Networks. Nodule and Irregular Cell Detection in Organs: Advancements in Medical Imaging. Enhancing Lung Disease Identification Through Ensemble Learning Methods. Unveiling Advanced Techniques for Feature Extraction in Medical Data.