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Deep: Real Life with Spinal Cord Injury
by Marcy Joy Epstein Travar Pettway"This project fits into the larger picture of excellence that we wish to accomplish in all dimensions of our health system: groundbreaking and dedicated research, compassionate clinical care, progressive education, and a welcoming environment that includes community with people with disabilities. In Deep, the writers and editors of this book realize this mission with accuracy and clarity." ---Denise G. Tate, Director of Research at the University of Michigan Model Spinal Cord Injury Care System People with spinal cord injuries experience life beyond their medical and rehabilitative journeys, but these stories are rarely told. Deep: Real Life with Spinal Cord Injury includes the stories of ten men and women whose lives have been transformed by spinal cord injury. Each essay challenges the stereotypes and misconceptions about SCI---with topics ranging from faith to humility to sex and manhood---offering a multitude of voices that weave together to create a better understanding of the diversity of disability and the uniqueness of those individuals whose lives are changed but not defined by their injuries. Life with SCI can be traumatic and ecstatic, uncharted and thrilling, but it always entails a journey beyond previous expectations. This volume captures this sea change, exploring the profound depths of SCI experience.
Deep Brain Stimulation: A New Frontier in Psychiatry
by Damiaan Denys, Matthijs Feenstra and Rick SchuurmanDeep Brain Stimulation: A New Frontier in Psychiatry provides an overview of current developments and the future possibilities of deep brain stimulation for patients with therapy-refractory psychiatric disorders. The side-by-side presentation of clinical applications and animal research provides a truly translational approach. Also included is a special chapter on the ethical issues involved in deep brain stimulation in psychiatry.Deep brain stimulation of selected brain areas has been shown to result in a substantial improvement of symptoms and quality of life in patients suffering from obsessive-compulsive disorder, major depressive disorder and drug addiction. Although it is still an experimental therapy and the number of psychiatric patients that are treated is low, its effectiveness and safety makes deep brain stimulation the most promising therapy for treating other serious and life-threatening psychiatric conditions.
Deep Brain Stimulation: Indications and Applications
by Kendall H. Lee Penelope S. Duffy Allan J. BieberDeep brain stimulation (DBS) is a widely used therapy for movement disorders such as Parkinson's disease, essential tremor, and dystonia. Its therapeutic success has led to the application of DBS for an increasing spectrum of conditions. However, the fundamental relationships between neural activation, neurochemical transmission, and clinical outcomes during DBS are not well understood. Drawing on the clinical and research expertise of the Mayo Clinic Neural Engineering Laboratories, this book addresses the history of therapeutic electrical stimulation of the brain, its current application and outcomes, and theories about its underlying mechanisms. It reviews research on measures of local stimulation–evoked neurochemical release, imaging research on stimulation-induced neural circuitry activation, and the state of the art on closed-loop feedback devices for stimulation delivery.
Deep Brain Stimulation: Indications and Applications
by Kendall H. Lee Penelope S. Duffy Allan J. BieberDeep brain stimulation (DBS) is a widely used therapy for movement disorders such as Parkinson's disease, essential tremor, and dystonia. Its therapeutic success has led to the application of DBS for an increasing spectrum of conditions. However, the fundamental relationships between neural activation, neurochemical transmission, and clinical outcomes during DBS are not well understood. Drawing on the clinical and research expertise of the Mayo Clinic Neural Engineering Laboratories, this book addresses the history of therapeutic electrical stimulation of the brain, its current application and outcomes, and theories about its underlying mechanisms. It reviews research on measures of local stimulation–evoked neurochemical release, imaging research on stimulation-induced neural circuitry activation, and the state of the art on closed-loop feedback devices for stimulation delivery.
Deep Brain Stimulation: A Case-based Approach
by Shilpa Chitnis, Pravin Khemani, Michael S. OkunDEEP BRAIN STIMULATION provides expert advice to the reader on selection guidelines and programming techniques for straight-forward as well as for challenging case management in movement and neuropsychiatric disorders. The collection offers a broad DBS experience that is delivered directly to you by leaders in neuromodulation. There are both common and uncommon case presentations and each case is accompanied by a literature review and pearls to improve your practice. The book improves fundamental DBS techniques as well as expands the skills necessary for troubleshooting more difficult presentations. The case-based problem-solving approach makes this a fun and practical read.
Deep Brain Stimulation: A Case-based Approach
DEEP BRAIN STIMULATION provides expert advice to the reader on selection guidelines and programming techniques for straight-forward as well as for challenging case management in movement and neuropsychiatric disorders. The collection offers a broad DBS experience that is delivered directly to you by leaders in neuromodulation. There are both common and uncommon case presentations and each case is accompanied by a literature review and pearls to improve your practice. The book improves fundamental DBS techniques as well as expands the skills necessary for troubleshooting more difficult presentations. The case-based problem-solving approach makes this a fun and practical read.
Deep Brain Stimulation for Neurological Disorders: Theoretical Background and Clinical Application
by Toru ItakuraChronic electrical stimulation of the brain has demonstrated excellent outcomes in patients with Parkinson’s disease and has recently also been applied to various other neurological diseases. This comprehensive, up-to-date textbook will meet the needs of all who wish to learn more about the application of deep brain stimulation and will provide a sound basis for safe and accurate surgery. The book comprises two main parts, the first of which presents relevant anatomical and functional background information on the basal ganglia, thalamus and other brain structures as well as on the mechanism of brain stimulation. The second part describes clinical studies on deep brain stimulation, covering results in a range of movement disorders and psychiatric diseases and also important aspects of instrumentation and technique. The authors are outstanding scientists and experts in the field from across the world.
Deep Brain Stimulation in Neurological and Psychiatric Disorders (Current Clinical Neurology)
by Daniel Tarsy Jerrold L. Vitek Philip A. Starr Michael S. OkunThis important book discusses today’s most current and cutting-edge applications of Deep Brain Stimulation (DBS). The book begins with reviews of the functional anatomy and physiology of motor and nonmotor aspects of the basal ganglia and their connections which underlie the application of DBS to neurological and psychiatric disorders. This is followed by proposed mechanisms of action of DBS based on functional neuroimaging and neurophysiologic studies in animals and man.
Deep Brain Stimulation Programming: Mechanisms, Principles and Practice
by Erwin B Montgomery, JrDeep brain stimulation programming (DBS) continues to grow as an effective therapy for a wide range of neurological and psychiatric disorders, helping patients reach optimal control of their disorder. With the technique finding so much success, the next question is how to make the complexities of post-operative programming cost-effective, especially when traditional medications and treatments can no longer do the job. The second edition of Deep Brain Stimulation Programming is fully revised and up-to-date with the latest technologies and focuses on post-operative programing, which no other text does. This book provides programmers with a foundation of the brain as an electrical device, focusing on the mechanisms by which neurons respond to electrical stimulation, how to control the stimulation and the regional anatomy, and the many variations that influence a patient's response to DBS. Dr. Montgomery explores new techniques of programming; including those based on stimulation frequency, closed-loop DBS, and the roles of oscillators in DBS; and new technological advances that make pre-existing theories of pathophysiology obsolete. Key Features of the Second Edition Include · Highlights post-operative deep brain stimulation; · Includes the most recent discoveries in deep brain stimulation programming; · Highly illustrated with figures for absorption of key programming and techniques; · Provides an appendix of additional resources available through the Greenville Neuromodulation Center.
Deep Brain Stimulation Programming: Mechanisms, Principles and Practice
by Erwin B Montgomery, JrDeep brain stimulation programming (DBS) continues to grow as an effective therapy for a wide range of neurological and psychiatric disorders, helping patients reach optimal control of their disorder. With the technique finding so much success, the next question is how to make the complexities of post-operative programming cost-effective, especially when traditional medications and treatments can no longer do the job. The second edition of Deep Brain Stimulation Programming is fully revised and up-to-date with the latest technologies and focuses on post-operative programing, which no other text does. This book provides programmers with a foundation of the brain as an electrical device, focusing on the mechanisms by which neurons respond to electrical stimulation, how to control the stimulation and the regional anatomy, and the many variations that influence a patient's response to DBS. Dr. Montgomery explores new techniques of programming; including those based on stimulation frequency, closed-loop DBS, and the roles of oscillators in DBS; and new technological advances that make pre-existing theories of pathophysiology obsolete. Key Features of the Second Edition Include · Highlights post-operative deep brain stimulation; · Includes the most recent discoveries in deep brain stimulation programming; · Highly illustrated with figures for absorption of key programming and techniques; · Provides an appendix of additional resources available through the Greenville Neuromodulation Center.
Deep Convolutional Neural Network for The Prognosis of Diabetic Retinopathy (Series in BioEngineering)
by A. Shanthini Gunasekaran Manogaran G. VadivuThis book discusses a detailed overview of diabetic retinopathy, symptoms, causes, and screening methodologies. Using a deep convolution neural network and visualizations techniques, this work develops a prognosis system used to automatically detect the diabetic retinopathy disease from captured retina images and help improve the prediction rate of diagnosis. This book gives the readers an understanding of the diabetic retinopathy disease and recognition process that helps to improve the clinical analysis efficiency. It caters to general ophthalmologists and optometrists, diabetologists, and internists who encounter diabetic patients and most prevalent retinal diseases daily.
Deep Imaging in Tissue and Biomedical Materials: Using Linear and Nonlinear Optical Methods
by Lingyan Shi Robert R. AlfanoThe use of light for probing and imaging biomedical media is promising for the development of safe, noninvasive, and inexpensive clinical imaging modalities with diagnostic ability. The advent of ultrafast lasers has enabled applications of nonlinear optical processes, which allow deeper imaging in biological tissues with higher spatial resolution. This book provides an overview of emerging novel optical imaging techniques, Gaussian beam optics, light scattering, nonlinear optics, and nonlinear optical tomography of tissues and cells. It consists of pioneering works that employ different linear and nonlinear optical imaging techniques for deep tissue imaging, including the new applications of single- and multiphoton excitation fluorescence, Raman scattering, resonance Raman spectroscopy, second harmonic generation, stimulated Raman scattering gain and loss, coherent anti-Stokes Raman spectroscopy, and near-infrared and mid-infrared supercontinuum spectroscopy. The book is a comprehensive reference of emerging deep tissue imaging techniques for researchers and students working in various disciplines.
Deep Learners and Deep Learner Descriptors for Medical Applications (Intelligent Systems Reference Library #186)
by Lakhmi C. Jain Sheryl Brahnam Loris Nanni Stefano Ghidoni Rick BrattinThis book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets (Advances in Computer Vision and Pattern Recognition)
by Le Lu Yefeng Zheng Gustavo Carneiro Lin YangThis book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (Advances in Computer Vision and Pattern Recognition)
by Le Lu Xiaosong Wang Gustavo Carneiro Lin YangThis book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings (Lecture Notes in Computer Science #10008)
by Gustavo Carneiro Diana Mateus Loïc Peter Andrew Bradley João Manuel R. S. Tavares Vasileios Belagiannis João Paulo Papa Jacinto C. Nascimento Marco Loog Zhi Lu Jaime S. Cardoso Julien CornebiseThis book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Deep Learning and Edge Computing Solutions for High Performance Computing: High Performance Computing And Emerging Healthcare Technologies (EAI/Springer Innovations in Communication and Computing)
by A. Suresh Sara PaivaThis book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.
Deep Learning and Other Soft Computing Techniques: Biomedical and Related Applications (Studies in Computational Intelligence #1097)
by Nguyen Hoang Phuong Vladik KreinovichThis book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies.Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques).The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.
Deep Learning for Biomedical Applications (Artificial Intelligence (AI): Elementary to Advanced Practices)
by Utku Kose Omer Deperlioglu D. Jude HemanthThis book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.
Deep Learning for Biomedical Applications (Artificial Intelligence (AI): Elementary to Advanced Practices)
by Utku Kose Omer Deperlioglu D. Jude HemanthThis book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.
Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
by Mourad ElloumiThis book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Deep Learning for Cancer Diagnosis (Studies in Computational Intelligence #908)
by Utku Kose Jafar AlzubiThis book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed.Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Deep Learning for Data Analytics: Foundations, Biomedical Applications, and Challenges
by Nilanjan Dey Himansu Das Chittaranjan PradhanDeep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications.Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networksProvides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Deep Learning for Healthcare Decision Making (River Publishers Series in Biomedical Engineering)
by Vishal Jain Jyotir Moy Chatterjee Ishaani Priyadarshini Fadi Al-TurjmanHealth 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.
Deep Learning for Healthcare Decision Making (River Publishers Series in Biomedical Engineering)
by Vishal Jain Jyotir Moy Chatterjee Ishaani Priyadarshini Fadi Al-TurjmanHealth 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.