Full Description
Health Informatics: Technologies and applications covers technological advances in healthcare that contribute to precision medicine and early detection of diseases. The editors explore the evolution of medical devices with attention to data management, patient safety and cost effectiveness.
Discussing health data and big data analytics for healthcare information, the editors also look at the application of AI in the healthcare arena, examining the concepts of machine learning for image sensing and the importance of feature selection, class imbalance, model robustness, and scalability.
With the advancement of telemedicine, the book will also examine cloud computing systems and the IoMT, with particular reference to cybersecurity concerns and the reliable management of electronic medical records.
The editors look at deploying these technologies for improved detection and therapeutic considerations for neurological and physiological conditions.
The book will be suitable for an audience of computer scientists and engineers particularly researchers working in healthcare technologies, AI/ML, computer science, data analysis or IoMT.
Contents
Chapter 1: Health Data Analytics and Big Data Applications
Chapter 2: Advances in Brain Tumour Detection Role of Feature Extraction and Machine Learning
Chapter 3: Understanding of Parkinson's disease pathology, diagnosis, therapies, and importance of biomarkers
Chapter 4: Amyotrophic Lateral Sclerosis (ALS) disease Genes, molecular pathology and diagnostic treatment
Chapter 5: Advances in Health Informatics for Obstructive Sleep Apnea Diagnosis and Management
Chapter 6: Optimized Audio Signal Reconstruction for AI-Driven Diagnosis of Chronic Respiratory Conditions
Chapter 7: Emotion Recognition using speech
Chapter 8: Enhanced Security and Privacy in IoMT: A Hierarchical Federated Learning Approach Using Dew-Cloud with HLSTM for Host
Chapter 9: Revolutionizing Pathological Assessments with Privacy-Centric Machine Learning Models
Chapter 10: Exploring the Synergy of SSL and IoMT in Breast Cancer Detection: A Theoretical Framework
Chapter 11: Proteomics techniques for characterizing microbial proteins
Chapter 12: Enhancing obstructive sleep apnea diagnosis with machine learning innovations and outcomes
Chapter 13: Ensemble and Hybrid Machine Learning Techniques: Theoretical Foundations, Differences, Applications and Healthcare Integration
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