Machine Learning

Methods and Applications to Brain Disorders
Erscheinungsjahr 2019
ISBN: 978-0-12-815739-8
Verlag: William Andrew Publishing

Buch, Englisch, 408 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 610 g

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.

Zielgruppe

Advanced students and researchers in behavioral neuroscience, psychology, psychiatry, psychology and neurology

Autoren/Hrsg.

Mechelli, Andrea

Vieira, Sandra

Fachgebiete

• Sozialwissenschaften Psychologie Allgemeine Psychologie Biologische Psychologie, Neuropsychologie
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Part I 1. Introduction to machine learning 2. Main concepts in machine learning 3. Applications of machine learning to brain disorders

Part II 4. Linear regression 5. Linear methods for classification 6. Support vector machine 7. Support vector regression 8. Multiple kernel learning 9. Deep neural networks 10. Convolutional neural networks 11. Autoencoders 12. Principal component analysis 13. K-means clustering

Part III 14. Dealing with missing data, small sample sizes, and heterogeneity 15. Working with high dimensional feature spaces: the example of voxel-wise encoding models 16. Multimodal integration 17. Bias, noise and interpretability in machine learning: from measurements to features 18. Ethical issues in the application of machine learning to brain disorders

Part IV 19. A step-by-step tutorial on how to build a machine learning model

Über Autor(innen)

Mechelli, Andrea
Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment.

Vieira, Sandra
Sandra Vieira is a postdoctoral researcher at the Institute Psychiatry, Psychology & Neuroscience (King's College London). After completing a degree in Psychology (2009) and a Masters in Clinical Psychology (2011) at the University of Coimbra, she joined the Institute Psychiatry, Psychology & Neuroscience. Here she obtained a Masters in Psychiatric Research in 2014 and a PhD in Psychosis Studies in 2019. Her research focuses on the integration of advanced machine learning methods and multi-modal neuroimaging to investigate the neural basis of mental illness and develop imaging-based clinical tools.