Deep Learning In Personalized Healthcare And Decision Support
Download Deep Learning In Personalized Healthcare And Decision Support full books in PDF, epub, and Kindle. Read online free Deep Learning In Personalized Healthcare And Decision Support ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads.
Author |
: Harish Garg |
Publisher |
: Elsevier |
Total Pages |
: 402 |
Release |
: 2023-07-20 |
ISBN-10 |
: 9780443194146 |
ISBN-13 |
: 0443194149 |
Rating |
: 4/5 (46 Downloads) |
Synopsis Deep Learning in Personalized Healthcare and Decision Support by : Harish Garg
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
Author |
: Adam Bohr |
Publisher |
: Academic Press |
Total Pages |
: 385 |
Release |
: 2020-06-21 |
ISBN-10 |
: 9780128184394 |
ISBN-13 |
: 0128184396 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Artificial Intelligence in Healthcare by : Adam Bohr
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
Author |
: Michael Mahler |
Publisher |
: Academic Press |
Total Pages |
: 302 |
Release |
: 2021-03-12 |
ISBN-10 |
: 9780323854320 |
ISBN-13 |
: 032385432X |
Rating |
: 4/5 (20 Downloads) |
Synopsis Precision Medicine and Artificial Intelligence by : Michael Mahler
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
Author |
: Wason, Ritika |
Publisher |
: IGI Global |
Total Pages |
: 248 |
Release |
: 2020-02-07 |
ISBN-10 |
: 9781799821021 |
ISBN-13 |
: 1799821021 |
Rating |
: 4/5 (21 Downloads) |
Synopsis Applications of Deep Learning and Big IoT on Personalized Healthcare Services by : Wason, Ritika
Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.
Author |
: Basant Agarwal |
Publisher |
: Academic Press |
Total Pages |
: 370 |
Release |
: 2020-01-14 |
ISBN-10 |
: 9780128190623 |
ISBN-13 |
: 0128190620 |
Rating |
: 4/5 (23 Downloads) |
Synopsis Deep Learning Techniques for Biomedical and Health Informatics by : Basant Agarwal
Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. - Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring - Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making - Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
Author |
: Juri Yanase |
Publisher |
: Infinite Study |
Total Pages |
: 51 |
Release |
: |
ISBN-10 |
: |
ISBN-13 |
: |
Rating |
: 4/5 ( Downloads) |
Synopsis A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments by : Juri Yanase
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Author |
: Arjun Panesar |
Publisher |
: Apress |
Total Pages |
: 390 |
Release |
: 2019-02-04 |
ISBN-10 |
: 9781484237991 |
ISBN-13 |
: 1484237994 |
Rating |
: 4/5 (91 Downloads) |
Synopsis Machine Learning and AI for Healthcare by : Arjun Panesar
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Author |
: Kenji Suzuki |
Publisher |
: Springer Nature |
Total Pages |
: 93 |
Release |
: 2019-10-24 |
ISBN-10 |
: 9783030338503 |
ISBN-13 |
: 3030338509 |
Rating |
: 4/5 (03 Downloads) |
Synopsis Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support by : Kenji Suzuki
This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
Author |
: Qiang Yang |
Publisher |
: Springer Nature |
Total Pages |
: 291 |
Release |
: 2020-11-25 |
ISBN-10 |
: 9783030630768 |
ISBN-13 |
: 3030630765 |
Rating |
: 4/5 (68 Downloads) |
Synopsis Federated Learning by : Qiang Yang
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Author |
: D. Jude Hemanth |
Publisher |
: Academic Press |
Total Pages |
: 322 |
Release |
: 2021-03-30 |
ISBN-10 |
: 9780128222720 |
ISBN-13 |
: 0128222727 |
Rating |
: 4/5 (20 Downloads) |
Synopsis Handbook of Decision Support Systems for Neurological Disorders by : D. Jude Hemanth
Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. - Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders - Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer's disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection - Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks - Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders