Deep Learning Approaches for Healthcare Data Analysis and Decision Making

Know about the book -

Deep Learning Approaches for Healthcare Data Analysis and Decision Making is written by Ashish Bagwari and published by Academic Press. It's available with International Standard Book Number or ISBN identification 0443437998 (ISBN 10) and 9780443437991 (ISBN 13).

Deep Learning Approaches for Healthcare Data Analysis and Decision Making is an essential resource for data scientists, machine learning engineers, health informaticians, and computer scientists eager to harness the power of deep learning in the healthcare sector. This book demystifies complex data-driven technologies, providing a clear framework for integrating advanced analytics into healthcare practices. With a focus on practical applications, the authors present a comprehensive digital transformation methodology that empowers readers to tackle the multifaceted challenges of healthcare data management. By leveraging deep learning techniques, readers will learn to analyze vast datasets, identify critical patterns, and develop predictive models that enhance diagnosis and treatment strategies while ensuring compliance with stringent data regulations. The book also addresses the pressing need for ethical AI practices, emphasizing patient privacy and data security. Real-world case studies illustrate how to implement personalized healthcare solutions and foster interdisciplinary collaboration, breaking down silos in knowledge and practice. Moreover, it explores innovative business models for sustainable AI integration, offering actionable insights for healthcare providers. This resource equips professionals with the tools to drive innovation, improve patient outcomes, and navigate the complexities of digital transformation in healthcare, making it a must-read for anyone at the intersection of technology and healthcare. - Integrates deep learning and AI into healthcare practices, addressing data management and workflow optimization - Illustrates practical examples to the successful application of deep learning techniques in various healthcare settings - Insights into developing and implementing predictive models to enhance diagnosis and treatment strategies - Approaches to identify and address biases in predictive models to enhance trust and accountability in AI-driven decisions - Presents tools and methodologies for managing and analyzing large healthcare datasets to derive meaningful insights and improve decision-making