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…mehr
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.
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Victor E. Staartjes: Dr. Victor Staartjes is the group leader of the Machine Intelligence in Clinical Neuroscience (MICN) Laboratory and a neurosurgery resident at the University Hospital Zurich under Prof. L. Regli. Originating from Amsterdam, he received his medical degree from the University of Zurich and is studying for a PhD in clinical machine learning at the Vrije Universiteit Amsterdam. Dr. Staartjes' research interests are in applications of machine learning to medical imaging and clinical prediction modeling, as well as robotic neurosurgery and personalized / precision medicine. Luca Regli: Prof. Luca Regli studied medicine at the University of Lausanne, he trained with Nicolas de Tribolet and obtained board certification in neurosurgery. At the renowned Mayo Clinic in Rochester, USA he specialized in the microsurgical treatment of complex intracranial lesions. In 2008 he was called as a full professor and chairman of neurosurgery at the University Medical Center of Utrecht, the Netherlands. In 2012 the University of Zurich nominated him as full professor and the University Hospital Zurich invited him to chair the Department of Neurosurgery, following into the steps of famous predecessors of Prof. Krayenbuehl, Prof. Yäargil, Prof. Yonekawa, and Prof. Bertalanffy, which is a renowned international reference center for cerebrovascular diseases, neuro-oncology and functional neurosurgery. Prof. Regli has developed his research interests driven by clinical questions in the domain of cerebral ischemia, cerebral metabolism, cerebral homeostasis and edema as well as in cutting-edge surgical techniques for cerebral revascularization and intra-operative imaging. The academic activity is reflected in over 265 publications in peer reviewed journals and 18 chapters in textbooks. As one of the worldwide leading experts in neurosurgery he is regularly invited as speaker at meetings all over the world. The clinical expertise is reflected in the daily management of patients with cerebrovascular lesions as well as brain tumors. He has personally treated microsurgically more than 1000 patients with cerebral aneurysms. As a recognized leading expert for management of vascular lesions he regularly gets referrals of patients with complex vascular lesions. Carlo Serra Dr. Carlo Serra is an assistant professor of neurosurgery and senior neurosurgeon at the University Hospital Zurich and co-leads the MICN Laboratory. Originating from Venice, Dr. Serra completed his medical studies and part of residency in Milan. He then moved to Zurich where he trained with Prof. L. Regli. During a fellowship with Prof. U. Türe and Prof. M.G. Yäargil in Istanbul, he developed a special expertise in brain tumor and skull base surgery as well as microneurosurgical anatomy, specifically white matter fiber dissection. He is currently responsible for the neuro-oncology and skull base programs of the Department of Neurosurgery of the University Hospital Zurich. "
Inhaltsangabe
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.
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.
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.
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.
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