| Description |
1 online resource |
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- List of Contributors -- List of Abbreviations -- Chapter 1: Amalgamation of Deep Learning in Healthcare Systems -- 1.1: Introduction to Deep Learning -- 1.2: Deep Learning in Healthcare -- 1.3: Artificial Intelligence in the Healthcare System -- 1.4: Machine Learning in Healthcare -- 1.5: Natural Language Processing (NLP) in Healthcare -- 1.6: Deep Learning Models -- 1.6.1: Interpretation of deep learning models in medical images |
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1.6.1.1: Convolutional neural networks (CNNs) -- 1.6.1.2: Recurrent neural networks (RNNs) -- 1.6.1.3: Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) -- 1.6.1.4: Deep neural network (DNN) -- 1.6.1.5: Generative adversarial network (GAN) -- 1.7: Radiologic Applications using Deep Learning -- 1.7.1: Image classification -- 1.7.2: Object detection -- 1.7.3: Image segmentation and registration -- 1.7.4: Image generation -- 1.7.5: Image transformation -- 1.7.5.1: Without the use of a generative adversarial network, image to image translation is possible |
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1.7.5.2: GAN for image-to-image translation -- 1.8: Predictive Analysis using Deep Learning and Machine Learning -- 1.9: Clinical Trials using Deep Learning -- 1.10: Applications of Deep Learning in the Healthcare System -- 1.10.1: Drug discovery -- 1.10.2: Medical imaging -- 1.10.3: Insurance fraud -- 1.10.4: Alzheimer's disease -- 1.10.5: Genome -- 1.10.6: Healthcare data analytics -- 1.10.7: Mental health chatbots -- 1.10.8: Personalized medical treatments -- 1.10.9: Prescription audit -- 1.10.10: Responding to patient queries -- Conclusion -- References |
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Chapter 2: Deep Neural Network Architecture and Applications in Healthcare -- 2.1: Introduction -- 2.2: Deep Neural Network -- 2.3: Deep Learning Architectures Applied in the Healthcare Field -- 2.3.1: Alzheimer's disease -- 2.3.2: Brain mris -- 2.3.3: Osteoarthritis -- 2.3.4: Breast cancer -- 2.3.5: Diabetic retinopathy -- 2.3.6: Forecasting type of medicine based on patient history -- 2.3.7: Forecasting diseases through patient's clinical status -- 2.3.8: Forecasting suicide -- 2.3.9: Forecasting readmission of patients after the discharge -- 2.3.10: Forecasting disease from lab test |
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2.3.11: Forecasting the quality of sleep by awake time activities -- 2.4: Pneumonia Detection using Deep Learning from X-ray Images -- 2.4.1: Overview -- 2.4.2: Methodology -- 2.4.2.1: Visualizing the images -- 2.4.2.2: Resizing -- 2.4.3: Results -- 2.4.3.1: ROC curve -- 2.4.3.2: Confusion matrix -- Conclusion -- References -- Chapter 3: The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare -- 3.1: Introduction -- 3.2: A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare |
| Summary |
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 |
| Notes |
Dr. Vishal Jain is presently working as an Associate Professor at Sharda University, Greater Noida, Uttar Pradesh. Previously, he worked for several years as an Associate Professor at Bharati Vidyapeeth Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 14 years of experience in academics. He obtained a PhD (CSE), MTech (CSE), MBA (HR), MCA, MCP, and CCNA. He has more than 370 research citation indices with Google Scholar (h-index score 9 and i10 index 9). He has published more than 70 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 10 books for various reputed publishers, including Springer, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald, and IGI-Global. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012-13 from the Computer Society of India, Best Faculty Award for the year 2017, and Best Researcher Award for the year 2019 from BVICAM, New Delhi. Jyotir Moy Chatterjee is currently working as an Assistant Professor in the Department of Information Technology at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. Previously, he worked as an Assistant Professor in the Department of Computer Science Engineering at G. D. Rungta College of Engineering & Technology (Chhattisgarh Swami Vivekananda Technical University), Bhilai, India. He received an MTech in Computer Science and Engineering from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha in 2016, and a BTech in Computer Science and Engineering from Dr. MGR Educational & Research Institute, Maduravoyal, Chennai in 2013. His research interest includes Machine Learning and the Internet of Things. He is also the Young Ambassador of the Scientific Research Group of Egypt (SRGE) for 2020-2021. He has published 21 SCIE/Scopus indexed international journals papers, 3 international Scopus indexed conference papers, 3 authored books, 14 edited books, 2 theses converted into books, 16 book chapters, and 1 patent. He is serving as an editorial board member of various reputed journals of IGI Global and serving as a reviewer for various reputed journals & international conferences of Elsevier, Springer, and IEEE. Ishaani Priyadarshini is working in the Faculty School of Information, UC Berkeley, California and also a PhD Candidate at the University of Delaware, USA. She obtained her Master's Degree in Cybersecurity from the University of Delaware. Before that, she completed her Bachelor's degree in Computer Science Engineering and a Master's degree in Information Security from Kalinga Institute of Industrial Technology, India. She has authored several book chapters for reputed publishers and is also an authored several publications for SCIE indexed journals. As a certified reviewer, she conducts peer review research papers for prestigious IEEE, Elsevier, and Springer journals, and is a part of the Editorial Board for International Journal of Information Security and Privacy (IJISP). Her areas of research include cybersecurity, artificial intelligence, and HCI. Prof. Dr. Fadi Al-Turjman received his PhD in computer science from Queen's University, Canada, in 2011. He is a full professor, Associate Dean for Research, Head of the Department of Artificial Intelligence Engineering research, and Center Director at Near East University, Nicosia, Cyprus. Professor Al-Turjman is a leading authority in the areas of smart/intelligent IoT systems, wireless, and mobile network architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT). His publication history spans over 350 SCI/E publications, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 40 books about cognition, security, and wireless sensor network deployments in smart IoT environments, which have been published by well-reputed publishers such as Taylor and Francis, Elsevier, IET, and Springer. He has received much recognition and several best paper awards at top international conferences. He also received the prestigious Best Research Paper Award from Elsevier Computer Communications Journal for the period 2015-2018, in addition to the Top Researcher Award for 2018 at Antalya Bilim University, Turkey. Professor Al-Turjman has led several international symposia and workshops in flagship communication society conferences. Currently, he serves as book series editor and the lead guest/associate editor for several top tier journals, including the IEEE Communications Surveys and Tutorials (IF 23.9) and the Elsevier Sustainable Cities and Society (IF 5.7), in addition to organizing international conferences and symposiums on the most up to date research topics in AI and IoT |
| Subject |
Deep learning (Machine learning)
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Medical care -- Decision making -- Data processing
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Artificial intelligence -- Medical applications.
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Deep Learning
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Artificial intelligence -- Medical applications
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Deep learning (Machine learning)
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Jain, Vishal, 1983-
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| ISBN |
9788770223881 |
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8770223882 |
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9781003373261 |
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1003373267 |
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9781000846546 |
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1000846547 |
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1000846520 |
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9781000846522 |
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