Download or read book Machine Learning in Action Stroke Diagnosis and Outcome Prediction written by Ramin Zand and published by Frontiers Media SA. This book was released on 2022-08-18 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Decision Support in Stroke written by Fabien Scalzo and published by Frontiers Media SA. This book was released on 2020-07-09 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Pitfalls in the Diagnosis of Neurological Disorders written by Ambar Chakravarty and published by Jaypee Brothers Medical Publishers. This book was released on 2022-10-31 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate diagnosis of neurological disorders can often be difficult due to the complex nature of the nervous system. Whilst technological advances have greatly improved diagnostic and interpretation techniques, errors can still occur, which consequently result in mistakes in therapeutic care. This book is a guide to diagnostic strategies for a multitude of common and less common neurological disorders. Scenarios are set in both a clinical and an intensive care setting. Divided into 13 sections, the text begins with an overview of general neurology. Each of the following sections examines disorders in a different part of the nervous system. Diagnostic processes are evaluated and potential areas where a clinician may make a mistake, are examined and explained in depth. The final section presents 15 authentic cases covering diagnostic challenges in critical care units, all provided by leading experts from the University of Texas Southwestern Medical Centre, USA. With contributions from internationally recognised neurologists, this comprehensive text is highly illustrated with neurological images, and many chapters feature additional editorial notes and appendices. Access to a selection of clinical videos via a QR code is also provided with this book.

Download or read book Machine Learning and Deep Learning in Neuroimaging Data Analysis written by Anitha S. Pillai and published by CRC Press. This book was released on 2024-02-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Download or read book Big data analytics to advance stroke and cerebrovascular disease A tool to bridge translational and clinical research written by Alexis Netis Simpkins and published by Frontiers Media SA. This book was released on 2023-12-26 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine learning in data analysis for stroke endovascular therapy written by Benjamin Yim and published by Frontiers Media SA. This book was released on 2023-09-05 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an estimated global incidence of 11 million patients per year, research involving ischemic stroke requires the collection and analysis of massive data sets affected by innumerable variables. Landmark studies that have historically shaped the foundation of our understanding of ischemic stroke and the development of management protocols have been derived from only a miniscule fraction of a percent of the entire population due to feasibility and capability. Machine learning provides an opportunity to capture data from an extraordinarily larger cohort size, which can be applied to training models to formulate algorithms to forecast outcomes with unparalleled accuracy and efficiency. The paradigm-shifting integration of machine learning in other industries, i.e. robotics, finance, and marketing, foreshadows its inevitable application to large population-based clinical research and practice. While prior multi-center studies have relied heavily on catalogued datasets requiring substantial manpower, the recent development of modern statistical methods can potentially expand the available quantity and quality of clinical data. In conjunction with data mining, machine learning has allowed automated extraction of clinical information from imaging, surgical videos, and electronic medical records to identify previously unseen patterns and create prediction models. Recently, it’s use in real-time detection of large vessel occlusion has streamlined health care delivery to a level of efficiency previously unmatched. The application of machine learning in ischemic stroke research – data acquisition, image evaluation, and prediction models – has the potential to reduce human error and increase reproducibility, accuracy, and precision with an unprecedented degree of power. However, one of the challenges with this integration remains the methods in which machine learning is utilized. Given the novelty of machine learning in clinical research, there remains significant variations in the application of machine learning tools and algorithms. The focus of the research topic is to provide a platform to compare the merits of various learning approaches – supervised, semi-supervised, unsupervised, self-learning – and the performances of various models.

Download or read book Neurocritical Care An Issue of Critical Care Clinics E Book written by Lori Shutter and published by Elsevier Health Sciences. This book was released on 2022-11-06 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this issue of Critical Care Clinics, guest editors Drs. Lori Shutter and Deepa Malaiyandi bring their considerable expertise to the topic of Neurocritical Care, a rapidly growing specialty of complex care. Top experts in the field provide up-to-date articles on important clinical trials and evidence-based care of the critically ill patient with neurological injury. Contains 16 practice-oriented topics including current management of acute ischemic stroke; status epilepticus: a neurological emergency; neurotrauma and ICP management; neuropharmacology in the ICU; artificial intelligence and big data science in neurocritical care; and more. Provides in-depth clinical reviews on neurocritical care, offering actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

Download or read book Machine Learning for Healthcare Applications written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-04-13 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

Download or read book Machine Learning and Deep Learning Techniques for Medical Science written by K. Gayathri Devi and published by CRC Press. This book was released on 2022-05-11 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Download or read book Brain Stroke Prediction using Machine Learning Techniques A Comparative Study written by R. Balamurugan and published by GRIN Verlag. This book was released on 2023-10-05 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Study from the year 2023 in the subject Computer Science - Bioinformatics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The use of machine learning for stroke prediction represents a powerful tool in enhancing patient care and reducing stroke-related mortality and disability. By focusing on key risk factors and leveraging extensive healthcare data, machine learning can substantially improve the accuracy and effectiveness of stroke prediction. This project aims to harness the potential of machine learning to better identify individuals at high risk of suffering a stroke and provide them with early, targeted interventions, ultimately saving lives and improving patient outcomes. The importance of predicting strokes cannot be overstated. Strokes are a leading cause of mortality and disability worldwide. Early detection and prevention can have a substantial impact on patient outcomes. Leveraging machine learning algorithms for stroke prediction can significantly improve the accuracy and efficacy of identifying high-risk patients. The primary objective of this project is to develop a precise stroke prediction system that can recognize high-risk patients based on a wide range of risk factors, including age, gender, medical history, lifestyle choices, and genetic factors. By creating a reliable model for stroke prediction, healthcare professionals can administer early interventions, potentially reducing stroke incidence and improving patient outcomes. The project's scope includes analyzing electronic health record (EHR) data to identify the key elements essential for stroke prediction. EHRs contain valuable information, including patient demographics, medical history, clinical findings, and other factors relevant to constructing a stroke prediction model. Machine learning for stroke prediction involves several stages. Initially, a dataset of relevant variables potentially influencing stroke occurrence is identified. This dataset may encompass demographic details, clinical information, laboratory tests, medical images, genetic data, and lifestyle factors. Subsequently, the dataset is cleaned and preprocessed to remove noise and inconsistencies. A machine learning algorithm is chosen, and the data is divided into training and testing groups. The algorithm is trained using the training data to identify patterns and relationships between variables and stroke occurrence. Once the model is trained, it is evaluated using the testing data to assess its performance.

Download or read book Machine Learning and Deep Learning Techniques for Medical Image Recognition written by Ben Othman Soufiene and published by CRC Press. This book was released on 2023-12-01 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of 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 and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Download or read book Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence written by Anitha S. Pillai and published by Academic Press. This book was released on 2022-02-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Mobile Technology for Adaptive Aging written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-10-25 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: To explore how mobile technology can be employed to enhance the lives of older adults, the Board on Behavioral, Cognitive, and Sensory Sciences of the National Academies of Sciences, Engineering, and Medicine commissioned 6 papers, which were presented at a workshop held on December 11 and 12, 2019. These papers review research on mobile technologies and aging, and highlight promising avenues for further research.

Download or read book Machine Learning in Clinical Neuroscience written by Victor E. Staartjes and published by Springer Nature. This book was released on 2021-12-03 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence and Machine Learning in Healthcare written by Ankur Saxena and published by Springer Nature. This book was released on 2021-05-06 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

Download or read book Big Data and Artificial Intelligence for Healthcare Applications written by Ankur Saxena and published by CRC Press. This book was released on 2021-06-15 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Download or read book Machine Learning in Medicine written by Ayman El-Baz and published by CRC Press. This book was released on 2021-08-04 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems. New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding. This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.