Machine Learning In Clinical Decision Making
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Machine Learning In Clinical Decision Making
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Author : Tyler John Loftus
language : en
Publisher: Frontiers Media SA
Release Date : 2023-09-07
Machine Learning In Clinical Decision Making written by Tyler John Loftus and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-07 with Medical categories.
Artificial Intelligence In Decision Support Systems For Diagnosis In Medical Imaging
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Author : Kenji Suzuki
language : en
Publisher: Springer
Release Date : 2018-01-09
Artificial Intelligence In Decision Support Systems For Diagnosis In Medical Imaging written by Kenji Suzuki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-09 with Technology & Engineering categories.
This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.
Guide To Health Informatics
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Author : Enrico Coiera
language : en
Publisher: CRC Press
Release Date : 2015-03-06
Guide To Health Informatics written by Enrico Coiera and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-06 with Medical categories.
This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible and understandable to the non-specialist, while providing everything that students of medical informatics need to know to accompany their course.
Artificial Intelligence In Healthcare
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Author : Adam Bohr
language : en
Publisher: Academic Press
Release Date : 2020-06-21
Artificial Intelligence In Healthcare written by Adam Bohr and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-21 with Computers categories.
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
Leveraging Data Science For Global Health
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Author : Leo Anthony Celi
language : en
Publisher: Springer Nature
Release Date : 2020-07-31
Leveraging Data Science For Global Health written by Leo Anthony Celi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Medical categories.
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Data Driven Clinical Decision Making Using Deep Learning In Imaging
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Author : M. F. Mridha
language : en
Publisher: Springer Nature
Release Date : 2024-08-13
Data Driven Clinical Decision Making Using Deep Learning In Imaging written by M. F. Mridha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-13 with Computers categories.
This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
Deep Learning In Personalized Healthcare And Decision Support
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Author : Harish Garg
language : en
Publisher: Elsevier
Release Date : 2023-07-20
Deep Learning In Personalized Healthcare And Decision Support written by Harish Garg and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-20 with Science categories.
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
Machine Learning In Clinical Neuroscience
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Author : Victor E. Staartjes
language : en
Publisher: Springer Nature
Release Date : 2021-12-03
Machine Learning In Clinical Neuroscience written by Victor E. Staartjes and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-03 with Medical categories.
This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.
Deep Learning For Healthcare Decision Making
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Author : Vishal Jain
language : en
Publisher: CRC Press
Release Date : 2023-02-10
Deep Learning For Healthcare Decision Making written by Vishal Jain and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-10 with Medical categories.
Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.
Machine Learning In Biomedical And Health Informatics
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Author : Ghouse B Batlapadu
language : en
Publisher: Academic Guru Publishing House
Release Date : 2025-01-06
Machine Learning In Biomedical And Health Informatics written by Ghouse B Batlapadu and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Study Aids categories.
Machine Learning in Biomedical and Health Informatics is a comprehensive guide that explores the transformative role of machine learning in healthcare. It begins with an introduction to ML, laying the groundwork by defining its scope, importance, and the types of learning methods. The book progresses to cover critical topics such as data preprocessing, supervised and unsupervised learning, deep learning, and natural language processing (NLP). Chapters delve into advanced applications like reinforcement learning, wearable technologies, and the integration of ML into clinical workflows. Ethical and privacy considerations are given due emphasis, reflecting the importance of trust and security in healthcare. Practical challenges and strategies for deploying ML models in real world settings are thoroughly discussed, ensuring readers understand the nuances of transitioning from theory to practice. Furthermore, the book explores future trends such as Explainable AI, federated learning, and emerging ML tools in biomedical research. Each chapter is structured to provide theoretical foundations, practical insights, and illustrative case studies, making complex concepts accessible to a broad audience. Whether you are a data scientist, clinician, or healthcare policymaker, this book equips you with the knowledge to harness ML’s potential. It empowers readers to address current challenges and envision innovative solutions that can revolutionize the delivery of healthcare services globally.