Data Analysis for Neurodegenerative Disorders

Data Analysis for Neurodegenerative Disorders PDF

Author: Deepika Koundal

Publisher: Springer Nature

Published: 2023-05-31

Total Pages: 267

ISBN-13: 9819921546

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This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: ● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. ● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. ● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases

Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases PDF

Author: Katherine E. Irimata

Publisher: JHU Press

Published: 2020-05-05

Total Pages: 481

ISBN-13: 1421436728

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A statistics textbook that delivers essential data analysis techniques for Alzheimer's and other neurodegenerative diseases. Alzheimer's disease is a devastating condition that presents overwhelming challenges to patients and caregivers. In the face of this relentless and as-yet incurable disease, mastery of statistical analysis is paramount for anyone who must assess complex data that could improve treatment options. This unique book presents up-to-date statistical techniques commonly used in the analysis of data on Alzheimer's and other neurodegenerative diseases. With examples drawn from the real world that will make it accessible to disease researchers, practitioners, academics, and students alike, this volume • presents code for analyzing dementia data in statistical programs, including SAS, R, SPSS, and Stata • introduces statistical models for a range of data types, including continuous, categorical, and binary responses, as well as correlated data • draws on datasets from the National Alzheimer's Coordinating Center, a large relational database of standardized clinical and neuropathological research data • discusses advanced statistical methods, including hierarchical models, survival analysis, and multiple-membership • examines big data analytics and machine learning methods Easy to understand but sophisticated in its approach, Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases will be a cornerstone for anyone looking for simplicity in understanding basic and advanced statistical data analysis topics. Allowing more people to aid in analyzing data—while promoting constructive dialogues with statisticians—this book will hopefully play an important part in unlocking the secrets of these confounding diseases.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning PDF

Author: Rani, Geeta

Publisher: IGI Global

Published: 2020-10-16

Total Pages: 586

ISBN-13: 1799827437

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By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Imaging in Neurodegenerative Disorders

Imaging in Neurodegenerative Disorders PDF

Author: Luca Saba

Publisher: Oxford University Press, USA

Published: 2015

Total Pages: 585

ISBN-13: 0199671613

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This text summarizes the latest developments in imaging techniques and other new diagnostic methods as applied to the neurodegenerative disorders.

Neurodegenerative Diseases Biomarkers

Neurodegenerative Diseases Biomarkers PDF

Author: Philip V. Peplow

Publisher:

Published: 2021

Total Pages: 565

ISBN-13: 9781071617120

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This volume presents recent data on the latest achievements in new and emerging technologies for biomarkers and for innovations in their assessment. The chapters cover topics such as activation of microglia and macrophages in neurodegenerative diseases; oxidative stress and cellular dysfunction in neurodegenerative diseases; TSPO PET imaging as a biomarker of neuroinflammation in neurodegenerative disorders; and imaging biomarkers in Huntington's disease and amyotrophic lateral sclerosis. 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. Cutting-edge and comprehensive, Neurodegenerative Diseases Biomarkers: Towards Translating Research to Clinical Practice is a valuable resource for both experimental and clinical experts in the field of neurodegenerative diseases who are looking to expand their knowledge of novel biomarkers in different types of neurodegenerative diseases.

Handbook of Decision Support Systems for Neurological Disorders

Handbook of Decision Support Systems for Neurological Disorders PDF

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

Big Data in Psychiatry and Neurology

Big Data in Psychiatry and Neurology PDF

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

Neurodegeneration

Neurodegeneration PDF

Author: Dennis Dickson

Publisher: John Wiley & Sons

Published: 2011-09-09

Total Pages: 497

ISBN-13: 1444341235

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Most textbooks on neurodegenerative disorders have used a classification scheme based upon either clinical syndromes or anatomical distribution of the pathology. In contrast, this book looks to the future and uses a classification based upon molecular mechanisms, rather than clinical or anatomical boundaries. Major advances in molecular genetics and the application of biochemical and immunocytochemical techniques to neurodegenerative disorders have generated this new approach. Throughout most of the current volume, diseases are clustered according to the proteins that accumulate within cells (e.g. tau, α-synuclein and TDP-43) and in the extracellular compartments (e.g. β-amyloid and prion proteins) or according to a shared pathogenetic mechanism, such as trinucleotide repeats, that are a feature of specific genetic disorders. Chapters throughout the book conform to a standard lay-out for ease of access by the reader and are written by a panel of International Experts Since the first edition of this book, major advances have been made in the discovery of common molecular mechanisms between many neurodegenerative diseases most notably in the frontotemporal lobar degenerations (FTLD) and motor neuron disease or amyotrophic lateral sclerosis. This book will be essential reading for clinicians, neuropathologists and basic neuroscientists who require the firm up-to-date knowledge of mechanisms, diagnostic pathology and genetics of Neurodegenerative diseases that is required for progress in therapy and management.

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