MACHINE LEARNING IN CLINICAL NEUROSCIENCE. FOUNDATIONS AND APPLICATIONS

Preface
• Foundations of machine learning-based clinical prediction modeling –
Part I: Introduction and general principles
• Foundations of machine learning-based clinical prediction modeling –
Part II: Generalization and Overfitting
• Foundations of machine learning-based clinical prediction modeling –
Part III: Evaluation and other points of significance
• Foundations of machine learning-based clinical prediction modeling –
Part IV: A practical approach to binary classification problems
• Foundations of machine learning-based clinical prediction modeling –
Part V: A practical approach to regression problems
• Supervised and unsupervised learning / clustering
• Introduction to Bayesian Modeling
• Introduction to Deep Learning
• Overview of algorithms for machine-learning based clinical prediction modelling
• Foundations of feature selection in clinical prediction modelling
• Dimensionality reduction: Foundations and applications in clinical neuroscience
• Machine learning-based survival modeling: Foundations and Applications
• Making clinical prediction models available: A brief introduction
• Machine Learning-based Clustering Analysis: Foundational Concepts, Methods, and Applications
• Introduction to Machine Learning in Neuroimaging
• Overview of machine learning algorithms in imaging
• Foundations of classification modeling based on neuroimaging
• Foundations of lesion-symptom mapping using machine learning
• Foundations of Machine Learning-Based Segmentation in Cranial Imaging
• Foundations of lesion detection using machine learning in clinical neuroimaging
• Foundations of multiparametric brain tumor imaging characterization
• Radiomics in clinical neuroscience - Overview
• Radiomic feature extraction: Methodological Foundations
• Complexity and interpretability in machine vision
• Foundations of intraoperative anatomical recognition using machine vision
• Machine Vision Foundations
• Natural Language Processing: Foundations and Applications in Clinical Neuroscience
• Foundations of Time Series Analysis
• Overview of algorithms for natural language processing and time series analysis
• History of machine learning in neurosurgery
• The AI doctor - considerations for AI-based medicine
• Ethics of Machine Learning-Based Predictive Analytics
• Predictive analytics in clinical practice: Pro and contra
• Review of machine vision applications in neuroophtalmology
• Prediction Model
• Prediction Model
• Prediction Model
• Topical Review of machine learning in intracranial aneurysm surgery
• Review of applications of machine learning in neuroimaging
• Prediction Model
• An overview of machine learning applications in the Neurointensive Care Unit
• Prediction Model
• Review of natural language processing in the clinical neurosciences
• Review of big data applications in the clinical neurosciences
• Radiomic features associated with extent of resection in glioma surgery.

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.

Features
• Focuses on methodological foundations of machine learning for clinicians with a focus on neuroscientists
• Discusses clinical applications of machine intelligence for improving patient care in clinical neuroscience
• Contributions written by a wide array of experienced machine learning groups