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  • Book
    Victor E. Staartjes, Luca Regli, Carlo Serra, editors.
    Summary: 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.

    Contents:
    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.
    Digital Access Springer 2022