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Machine Learning

Author: Andrea Mechelli

Publisher: Academic Press

Published: 2019-11-14

Total Pages: 412

ISBN-13: 0128157402

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Machine Learning

Author: Andrea Mechelli

Publisher: Academic Press

Published: 2019-11

Total Pages: 400

ISBN-13: 9780128157398

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Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders. Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches. Covers the main methodological challenges in the application of machine learning to brain disorders. Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python.

Machine Learning for Brain Disorders

Author: Olivier Colliot

Publisher: Springer Nature

Published: 2023-07-24

Total Pages: 1058

ISBN-13: 1071631950

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This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.

Early Detection of Neurological Disorders Using Machine Learning Systems

Author: Paul, Sudip

Publisher: IGI Global

Published: 2019-06-28

Total Pages: 376

ISBN-13: 1522585680

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While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen. Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.

Recent Progress in Brain and Cognitive Engineering

Author: Seong-Whan Lee

Publisher: Springer

Published: 2015-10-27

Total Pages: 213

ISBN-13: 9401772398

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For ‘Recent Progress in Brain and Cognitive Engineering’ Brain and Cognitive Engineering is a converging study field to derive a better understanding of cognitive information processing in the human brain, to develop “human-like” and neuromorphic artificial intelligent systems and to help predict and analyze brain-related diseases. The key concept of Brain and Cognitive Engineering is to understand the Brain, to interface the Brain, and to engineer the Brain. It could help us to understand the structure and the key principles of high-order information processing on how the brain works, to develop interface technologies between a brain and external devices and to develop artificial systems that can ultimately mimic human brain functions. The convergence of behavioral, neuroscience and engineering research could lead us to advance health informatics and personal learning, to enhance virtual reality and healthcare systems, and to “reverse engineer” some brain functions and build cognitive robots. In this book, four different recent research directions are presented: Non-invasive Brain-Computer Interfaces, Cognitive- and Neural-rehabilitation Engineering, Big Data Neurocomputing, Early Diagnosis and Prediction of Neural Diseases. We cover numerous topics ranging from smart vehicles and online EEG analysis, neuroimaging for Brain-Computer Interfaces, memory implantation and rehabilitation, big data computing in cultural aspects and cybernetics to brain disorder detection. Hopefully this will provide a valuable reference for researchers in medicine, biomedical engineering, in industry and academia for their further investigations and be inspiring to those who seek the foundations to improve techniques and understanding of the Brain and Cognitive Engineering research field.

Artificial Intelligence for Neurological Disorders

Author: Ajith Abraham

Publisher: Academic Press

Published: 2022-09-23

Total Pages: 434

ISBN-13: 0323902782

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Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. Discusses various AI and ML methods to apply for neurological research Explores Deep Learning techniques for brain MRI images Covers AI techniques for the early detection of neurological diseases and seizure prediction Examines cognitive therapies using AI and Deep Learning methods

Handbook of Decision Support Systems for Neurological Disorders

Author: Hemanth D. Jude

Publisher: Academic Press

Published: 2021-03-30

Total Pages: 320

ISBN-13: 0128222727

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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

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Author: Anitha S. Pillai

Publisher: Academic Press

Published: 2022-02-23

Total Pages: 356

ISBN-13: 0323886264

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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

Big Data in Psychiatry and Neurology

Author: Ahmed Moustafa

Publisher: Academic Press

Published: 2021-06-11

Total Pages: 386

ISBN-13: 0128230029

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Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics

Intelligent Data Analysis

Author: Deepak Gupta

Publisher: John Wiley & Sons

Published: 2020-07-13

Total Pages: 428

ISBN-13: 1119544459

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This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.