Machine Learning for Brain Disorders

Olivier Colliot

ISBN 10: 1071631977 / ISBN 13: 9781071631973

Verlag: Springer US Jul 2023, 2023

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 1080 pp. Englisch.

Bestandsnummer des Verkäufers 9781071631973

Bibliografische Details

• Titel: Machine Learning for Brain Disorders
• Verlag: Springer US Jul 2023
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1071631977
• ISBN-13: 9781071631973
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 254B x 178 H x 54 T Millimeter Breite: 254 mm, Höhe: 178 mm, Tiefe: 54 mm
• Gewicht: 2226 Gramm

Über diesen Titel

Inhaltsangabe

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.