Advanced Machine Learning For Complex Medical Data Analysis
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Advanced Machine Learning for Complex Medical Data Analysis
Author: Saumendra Kumar Mohapatra, Mihir Narayan Mohanty, Rashmita Khilar
language: en
Publisher: Bentham Science Publishers
Release Date: 2025-05-13
Advanced Machine Learning for Complex Medical Data Analysis is a definitive guide to leveraging machine learning to solve critical challenges in medical data analysis. This book discusses cutting-edge methodologies, from predictive modeling to neural networks, tailored to address the unique complexities of medical and healthcare data. It combines theoretical frameworks with practical applications, ensuring readers gain a comprehensive understanding of both concepts and real-world implementations. The book covers diverse topics, including medical image denoising, the transformative role of GANs, IoT applications in healthcare, early disease detection using speech data, and COVID detection using autoencoders. It also explores the impact of big data, statistical approaches to medical analytics, and public health improvements through technology. Key Features: - Practical insights into deploying advanced machine learning models for healthcare. - Real-world case studies on diverse diseases and datasets. - Cutting-edge topics like explainable AI, federated learning, and ethical considerations. - Methods for improving data accuracy, efficiency, and privacy.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Author: Ilker Ozsahin
language: en
Publisher: Bentham Science Publishers
Release Date: 2021-11-18
This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.