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Author : Xiaozheng Liu,Zhi Xu,Weikai Li,Zhen Zhou
Publisher : Frontiers Media SA
Page : 124 pages
File Size : 41,8 Mb
Release : 2023-06-27
Category : Medical
ISBN : 9782832526989
Author : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 40,7 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404
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
Author : Mamta Mittal,Lalit Mohan Goyal
Publisher : Springer Nature
Page : 310 pages
File Size : 47,7 Mb
Release : 2022-05-20
Category : Technology & Engineering
ISBN : 9789811917240
This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
Author : Ives Cavalcante Passos,Benson Mwangi,Flávio Kapczinski
Publisher : Springer
Page : 180 pages
File Size : 55,8 Mb
Release : 2019-02-12
Category : Medical
ISBN : 9783030035532
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide 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. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.
Author : Olivier Colliot
Publisher : Springer Nature
Page : 1058 pages
File Size : 49,8 Mb
Release : 2023-07-24
Category : Medical
ISBN : 9781071631959
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.
Author : Sudhakar Selvaraj,Paolo Brambilla,Jair C. Soares
Publisher : Cambridge University Press
Page : 295 pages
File Size : 50,6 Mb
Release : 2021-01-07
Category : Medical
ISBN : 9781108427128
Offering up-to-date information on brain imaging in mood disorders, this book is an invaluable resource for mental health professionals.
Author : Prableen Kaur
Publisher : Prableen Kaur
Page : 0 pages
File Size : 47,7 Mb
Release : 2023-03-14
Category : Electronic
ISBN : 5989236867
Techniques for Detecting Human Psychological Disorders Using Machine Learning and Soft Computing refers to the application of computational methods to identify and diagnose various mental health conditions in humans. This involves the use of machine learning and soft computing algorithms to analyze data and patterns related to psychological disorders, such as neuroimaging, speech analysis, and behavioral data. The objective of these techniques is to develop diagnostic tools and predictive models that can aid mental health professionals in the accurate and efficient diagnosis of psychological disorders. By using computational models to analyze data, these techniques have the potential to identify patterns that are not easily detectable by humans, leading to earlier and more accurate diagnoses, and ultimately improving the treatment outcomes for individuals with psychological disorders. The use of machine learning and soft computing in detecting human psychological disorders is a rapidly growing field of research with potential applications in clinical and non-clinical settings. By leveraging large amounts of data and complex algorithms, these techniques can assist in the early detection of mental health conditions, providing an opportunity for early intervention and prevention of more severe symptoms. These methods can also help mental health professionals to develop personalized treatment plans for patients based on their individual profiles, leading to more effective and efficient care. The use of machine learning and soft computing in this area can also help to reduce the stigma associated with mental health disorders by providing objective and data-driven diagnosis and treatment recommendations. However, the application of these techniques also raises ethical considerations, including issues related to privacy, bias, and the potential for misinterpretation of results. Despite these challenges, the continued development of machine learning and soft computing techniques for detecting psychological disorders holds great promise for improving mental health outcomes and quality of life for individuals with these conditions. Psychological disorders are chronic and critical human disorders that may affect the overall personality, behaviour and health of a person . A psychological disorder is a condition characterized by abnormal thoughts, behaviours and feelings that can have significant impact on person's everyday functioning and existence. As per Diagnostic and Statistical Manual of Mental Disorders (DSM-5), psychological disorders are classified into twenty main categories viz. neurodevelopment disorder, schizophrenia, psychotic disorders, depression, anxiety, trauma- and stressor-related disorders, obsessive-compulsive and related disorders, dissociative disorders, feeding and eating disorders, elimination disorders, sleep-wake disorders, sexual dysfunctions, gender dysphoria, disruptive, impulsive and conduct disorders, substance-related and the addictive disorders, neurocognitive disorders, personality disorders, paraphilic disorders and other mental disorders
Author : Eric Murillo-Rodriguez
Publisher : Academic Press
Page : 308 pages
File Size : 43,6 Mb
Release : 2021-10-09
Category : Medical
ISBN : 9780323903349
Methodological Approaches for Sleep and Vigilance Research examines experimental procedures used to study the sleep-wake cycle, with topics covered by world leaders in the field. The book focuses on techniques commonly used in the sleep field, including polysomnography, electrophysiology, single- and multi-unit spiking activity recording, brain stimulation, EEG power spectra, optogenetics, telemetry, and wearable and non-wearable tracking devices. Further chapters on imaging techniques, questionnaires for sleep assessment, genome-wide association studies, artificial intelligence and big data are also featured. This discussion of significant conceptual advances into experimental procedures is suitable for anyone interested in the neurobiology of sleep. Discusses current sleep research methodologies for experienced scientists Focuses on techniques that allow measurement or assessment for the sleep-wake cycle Outlines mainstream research techniques and experimental characteristics of their uses Includes polysomnography, deep brain stimulation, and more Reviews sleep-tracking devices, EEG and telemetry Covers artificial intelligence and big data in analysis
Author : Gopi Battineni,Mamta Mittal,Nalini Chintalapudi
Publisher : Springer Nature
Page : 345 pages
File Size : 47,8 Mb
Release : 2023-11-30
Category : Medical
ISBN : 9789819966370
This book presents a particular area of interest in computing psychiatry with the modelling of mood and anxiety disorders. It highlights various methods for building these models. Clinical applications are prevalent due to the growth and interaction of these multiple approaches. Besides, it outlines some original predictive and computational modelling ideas for enhancing psychological treatment interventions. Computational psychiatry combines multiple levels and types of computation with different data types to improve mental illness understanding, prediction, and treatment.
Author : Ahmed Moustafa
Publisher : Academic Press
Page : 386 pages
File Size : 40,8 Mb
Release : 2021-06-11
Category : Medical
ISBN : 9780128230022
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 : Sachi Nandan Mohanty,G. Nalinipriya,Om Prakash Jena,Achyuth Sarkar
Publisher : John Wiley & Sons
Page : 418 pages
File Size : 55,9 Mb
Release : 2021-04-13
Category : Computers
ISBN : 9781119791812
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.
Author : Yong-Ku Kim
Publisher : Springer Nature
Page : 641 pages
File Size : 52,6 Mb
Release : 2019-11-09
Category : Science
ISBN : 9789813297210
This book reviews key recent advances and new frontiers within psychiatric research and clinical practice. These advances either represent or are enabling paradigm shifts in the discipline and are influencing how we observe, derive and test hypotheses, and intervene. Progress in information technology is allowing the collection of scattered, fragmented data and the discovery of hidden meanings from stored data, and the impacts on psychiatry are fully explored. Detailed attention is also paid to the applications of artificial intelligence, machine learning, and data science technology in psychiatry and to their role in the development of new hypotheses, which in turn promise to lead to new discoveries and treatments. Emerging research methods for precision medicine are discussed, as are a variety of novel theoretical frameworks for research, such as theoretical psychiatry, the developmental approach to the definition of psychopathology, and the theory of constructed emotion. The concluding section considers novel interventions and treatment avenues, including psychobiotics, the use of neuromodulation to augment cognitive control of emotion, and the role of the telomere-telomerase system in psychopharmacological interventions.
Author : Victor E. Staartjes,Luca Regli,Carlo Serra
Publisher : Springer Nature
Page : 343 pages
File Size : 44,8 Mb
Release : 2021-12-03
Category : Medical
ISBN : 9783030852924
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.
Author : Drozdstoy Stoyanov,Paolo Brambilla
Publisher : Frontiers Media SA
Page : 175 pages
File Size : 42,5 Mb
Release : 2020-08-21
Category : Electronic
ISBN : 9782889639540
Author : Adam Bohr,Kaveh Memarzadeh
Publisher : Academic Press
Page : 385 pages
File Size : 42,7 Mb
Release : 2020-06-21
Category : Computers
ISBN : 9780128184394
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
