BookVictor 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.