Building Multimodal Clinical Decision Support Systems Using Artificial Intelligence

Medical Content Based Retrieval for Clinical Decision Support

• Author : Hayit Greenspan
• Publisher : Springer
• Release Date : 2013-01-31
• Genre: Computers
• Pages : 145
• ISBN 10 : 3642366775

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection.

Medical Content Based Retrieval for Clinical Decision Support

• Author : Henning Mueller
• Publisher : Springer
• Release Date : 2012-02-21
• Genre: Computers
• Pages : 153
• ISBN 10 : 9783642284601

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2011, held in Toronto, Canada, in September 2011. The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions. The papers are divided on several topics on medical image retrieval with textual approaches, visual word based approaches, applications and multidimensional retrieval.

Medical Content Based Retrieval for Clinical Decision Support

• Author : Henning Müller
• Publisher : Springer
• Release Date : 2010-02-04
• Genre: Computers
• Pages : 121
• ISBN 10 : 9783642117695

This book constitutes the refereed proceedings of the first MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR_CBS 2009, held in London, UK, in September 2009. The 10 revised full papers were carefully reviewed and selected from numerous submissions. The papers are divide on several topics on medical image retrieval, clinical decision making and multimodal fusion.

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

• Author : Kenji Suzuki
• Publisher : Springer Nature
• Release Date : 2019-10-24
• Genre: Computers
• Pages : 93
• ISBN 10 : 9783030338503

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.

Big Data in Multimodal Medical Imaging

• Author : Ayman El-Baz
• Publisher : CRC Press
• Release Date : 2019-11-06
• Genre: Computers
• Pages : 341
• ISBN 10 : 9781351380720

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

• Author : Anitha S. Pillai
• Publisher : Academic Press
• Release Date : 2022-02-23
• Genre: Science
• Pages : 356
• ISBN 10 : 9780323886260

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer’s Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer’s Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

Artificial Intelligence and MRI Boosting Clinical Diagnosis

• Author : Antonio Napolitano
• Publisher : Frontiers Media SA
• Release Date : 2022-08-05
• Genre: Medical
• Pages : 322
• ISBN 10 : 9782889767199

Artificial Intelligence and Integrated Intelligent Information Systems

• Author : Xuan F. Zha
• Publisher : IGI Global
• Release Date : 2007-01-01
• Genre: Computers
• Pages : 479
• ISBN 10 : 9781599042497

Researchers in the evolving fields of artificial intelligence and information systems are constantly presented with new challenges. Artificial Intelligence and Integrated Intelligent Information Systems: Emerging Technologies and Applications provides both researchers and professionals with the latest knowledge applied to customized logic systems, agent-based approaches to modeling, and human-based models. Artificial Intelligence and Integrated Intelligent Information Systems: Emerging Technologies and Applications presents the recent advances in multi-mobile agent systems, the product development process, fuzzy logic systems, neural networks, and ambient intelligent environments among many other innovations in this exciting field.

Signal Processing Techniques for Computational Health Informatics

• Author : Md Atiqur Rahman Ahad
• Publisher : Springer Nature
• Release Date : 2020-10-07
• Genre: Technology & Engineering
• Pages : 334
• ISBN 10 : 9783030549329

This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

• Author : Suresh, Annamalai
• Publisher : IGI Global
• Release Date : 2020-06-26
• Genre: Computers
• Pages : 294
• ISBN 10 : 9781799835929

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

Artificial Intelligence Machine Learning in Nuclear Medicine and Hybrid Imaging

• Author : Patrick Veit-Haibach
• Publisher : Springer Nature
• Release Date : 2022-06-22
• Genre: Medical
• Pages : 210
• ISBN 10 : 9783031001192

This book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic. A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice. As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.

Big Data Analytics for Large Scale Multimedia Search

• Author : Stefanos Vrochidis
• Publisher : John Wiley & Sons
• Release Date : 2019-03-18
• Genre: Technology & Engineering
• Pages : 376
• ISBN 10 : 9781119376989

A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections. Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data. Addresses the area of multimedia retrieval and pays close attention to the issue of scalability Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios Includes tables, illustrations, and figures Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.

Clinical Decision Making for Improving Prognosis

• Author : Rong Liu (Surgical oncologist)
• Publisher : Springer Nature
• Release Date : 2022
• Genre: Decision making
• Pages : 196
• ISBN 10 : 9789811929526

With the advent of artificial intelligence and big data era, a new concept of clinical decision making for improving surgical outcomes was proposed, which emphasizes the optimal prognosis and best outcome for patients, makes full use of information technology such as artificial intelligence to reduce the uncertainty in the treatment process and the unevenness of the treatment level, and selects the most appropriate intervention means and intervention timing through objective evaluation. It will be helpful for surgeons to choose treatment options which will be effective to patients.

Digital Therapeutics for Mental Health and Addiction

• Author : Nicholas C. Jacobson
• Publisher : Academic Press
• Release Date : 2022-09-27
• Genre: Technology & Engineering
• Pages : 270
• ISBN 10 : 9780323885614

Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future presents the foundations of digital therapeutics with a broad audience in mind, ranging from bioengineers and computer scientists to those in psychology, psychiatry and social work. Sections cover cutting-edge advancements in the field, offering advice on how to successfully implement digital therapeutics. Readers will find sections on evidence for direct-to-consumer standalone digital therapeutics, the efficacy of integrating digital treatments within traditional healthcare settings, and recent innovations currently transforming the field of digital therapeutics towards experiences which are more personalized, adaptable and engaging. This book gives a view on current limitations of the technology, ideas for problem-solving the challenges of designing this technology, and a perspective on future research directions. For all readers, the content on cultural, legal and ethical dimensions of digital mental health will be useful. Gives a comprehensive overview of the field of digital therapeutics and research on their efficacy, effectiveness, scalability and cost-effectiveness Introduces novel directions in which digital therapeutics are currently being extended, including personalized interventions delivered in real-time Reviews important considerations surrounding digital therapeutics, including how they can be monetized and scaled, ethical issues, cultural adaptations, privacy and security concerns, and potential pitfalls

Explainable Artificial Intelligence for Cyber Security

• Author : Mohiuddin Ahmed
• Publisher : Springer Nature
• Release Date : 2022
• Genre: Artificial intelligence
• Pages : 283
• ISBN 10 : 9783030966300

This book presents that explainable artificial intelligence (XAI) is going to replace the traditional artificial, machine learning, deep learning algorithms which work as a black box as of today. To understand the algorithms better and interpret the complex networks of these algorithms, XAI plays a vital role. In last few decades, we have embraced AI in our daily life to solve a plethora of problems, one of the notable problems is cyber security. In coming years, the traditional AI algorithms are not able to address the zero-day cyber attacks, and hence, to capitalize on the AI algorithms, it is absolutely important to focus more on XAI. Hence, this book serves as an excellent reference for those who are working in cyber security and artificial intelligence.