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Author: Deepika Koundal Publisher: Springer Nature ISBN: 9819921546 Category : Medical Languages : en Pages : 267
Book Description
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.
Author: Deepika Koundal Publisher: Springer Nature ISBN: 9819921546 Category : Medical Languages : en Pages : 267
Book Description
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.
Author: Katherine E. Irimata Publisher: Johns Hopkins University Press ISBN: 142143671X Category : Medical Languages : en Pages : 481
Book Description
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.
Author: Katherine E. Irimata Publisher: JHU Press ISBN: 1421436728 Category : Medical Languages : en Pages : 481
Book Description
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.
Author: Rani, Geeta Publisher: IGI Global ISBN: 1799827437 Category : Medical Languages : en Pages : 586
Book Description
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.
Author: G. Ali Qureshi Publisher: Elsevier ISBN: 9780080489490 Category : Science Languages : en Pages : 794
Book Description
Oxidative stress is the result of an imbalance in pro-oxidant/antioxidant homeostasis that leads to the generation of toxic reactive oxygen species. Brain cells are continuously exposed to reactive oxygen species generated by oxidative metabolism, and in certain pathological conditions defense mechanisms against oxygen radicals may be weakened and/or overwhelmed. DNA is a potential target for oxidative damage, and genomic damage can contribute to neuropathogenesis. It is important therefore to identify tools for the quantitative analysis of DNA damage in models on neurological disorders. This book presents detailed information on various neurodegenerative disorders and their connection with oxidative stress. This information will provide clinicians with directions to treat these disorders with appropriate therapy and is also of vital importance for the drug industries for the design of new drugs for treatment of degenerative disorders. * Contains the latest information on the subject of neurodegenerative disorders * Reflects on various factors involved in degeneration and gives suggestions for how to tackle these problems
Author: Luca Saba Publisher: Oxford University Press, USA ISBN: 0199671613 Category : Medical Languages : en Pages : 585
Book Description
This text summarizes the latest developments in imaging techniques and other new diagnostic methods as applied to the neurodegenerative disorders.
Author: Ahmed Moustafa Publisher: Academic Press ISBN: 0128230029 Category : Medical Languages : en Pages : 386
Book Description
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
Author: Philip V. Peplow Publisher: ISBN: 9781071617120 Category : Biochemical markers Languages : en Pages : 565
Book Description
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.
Author: Paul, Sudip Publisher: IGI Global ISBN: 1522585680 Category : Medical Languages : en Pages : 376
Book Description
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.
Author: Deepika Koundal
Author: Deepika Koundal
Author: Katherine E. Irimata
Author: Katherine E. Irimata
Author: Rani, Geeta
Author: G. Ali Qureshi
Author: Luca Saba
Author: Ahmed Moustafa
Author: Loveleen Gaur
Author: Philip V. Peplow
Author: Paul, Sudip