Deep Learning in Personalized Healthcare and Decision Support
Deep Learning in Personalized Healthcare and Decision Support

Author: Harish Garg

Publisher: Elsevier

Published: 2023-07-20

Total Pages: 402

ISBN-13: 0443194149

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Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. - Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management - Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way - Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies

Deep Learning for Personalized Healthcare Services
Deep Learning for Personalized Healthcare Services

Author: Vishal Jain

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2021-10-25

Total Pages: 268

ISBN-13: 3110708124

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This book uncovers the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms, enabling the healthcare industry to develop meaningful and cost-effective services. This requires effective understanding, application and amalgamation of deep learning with several other computing technologies, such as machine learning, data mining, and natural language processing.

Fundamentals of Clinical Data Science
Fundamentals of Clinical Data Science

Author: Pieter Kubben

Publisher: Springer

Published: 2018-12-21

Total Pages: 219

ISBN-13: 3319997130

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This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Data-Driven Approach for Bio-medical and Healthcare
Data-Driven Approach for Bio-medical and Healthcare

Author: Nilanjan Dey

Publisher: Springer Nature

Published: 2022-10-27

Total Pages: 238

ISBN-13: 9811951845

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The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.

Precision Medicine and Artificial Intelligence
Precision Medicine and Artificial Intelligence

Author: Michael Mahler

Publisher: Academic Press

Published: 2021-03-12

Total Pages: 302

ISBN-13: 032385432X

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Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare

Author: Adam Bohr

Publisher: Academic Press

Published: 2020-06-21

Total Pages: 385

ISBN-13: 0128184396

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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

Revolutionizing Healthcare Treatment With Sensor Technology
Revolutionizing Healthcare Treatment With Sensor Technology

Author: Das, Sima

Publisher: IGI Global

Published: 2024-05-28

Total Pages: 399

ISBN-13:

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Traditional patient care and treatment approaches often lack the personalized and interactive elements necessary for effective healthcare delivery. This means that the healthcare industry must find innovative solutions to improve patient outcomes, enhance rehabilitation processes, and optimize resource utilization. There is a gap between the traditional approach and the need for innovation that highlights the importance of a comprehensive understanding of emerging technologies, including Kinect Sensor technology, and the potential to transform healthcare practices with this tech. Revolutionizing Healthcare Treatment With Sensor Technology addresses this critical need by thoroughly exploring how Kinect Sensor technology can revolutionize patient care and treatment methodologies. By repurposing and customizing Kinect Sensor for healthcare applications, this book showcases how depth-sensing cameras, infrared sensors, and advanced motion tracking can capture and interpret real-time patient movements and interactions. This book is ideal for healthcare professionals, hospital administrators, researchers, patients, caregivers, and healthcare technology developers seeking to leverage Kinect Sensor technology for enhanced healthcare delivery. Through detailed case studies and practical examples, experts can learn how to integrate Kinect Sensor into various medical settings to gain valuable insights into patients' physical capabilities, monitor their progress, and create personalized treatment plans.

AI AND BIOTECH IN PHARMACEUTICAL RESEARCH (Synergies in Drug Discovery)
AI AND BIOTECH IN PHARMACEUTICAL RESEARCH (Synergies in Drug Discovery)

Author: Dr. Alok Kumar Srivastav

Publisher: Namya Press

Published: 2024-08-25

Total Pages: 356

ISBN-13: 9355452845

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"AI and Biotech in Pharmaceutical Research: Synergies in Drug Discovery" offers a comprehensive exploration of the transformative role AI plays in modern drug discovery and development. The book delves into the integration of artificial intelligence with biotechnological advances, highlighting how these synergies are revolutionizing every stage of the pharmaceutical research process. From the basics of drug discovery to cutting-edge applications in personalized medicine and rare diseases, each chapter unravels the complexities of AI-driven approaches. It covers the impact of machine learning, predictive modeling, and computational biology, while also addressing ethical considerations, algorithmic bias, and regulatory challenges. Real-world case studies and success stories provide tangible examples of AI's potential to accelerate drug development and address unmet medical needs. The book also forecasts future trends, emphasizing the importance of interdisciplinary collaboration, innovative startups, and emerging technologies like blockchain. A must-read for professionals, researchers, and enthusiasts, this book presents a forward-looking view of how AI is reshaping the pharmaceutical landscape, driving innovation, and ultimately improving global health outcomes.