click 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 […]

Machine Learning in Clinical Neuroscience: Foundations and Applications

Length: 368 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2022-01-04
ISBN-10: 3030852911
ISBN-13: 9783030852917
Sales Rank: #0 (See Top 100 Books)

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follow link 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.

source site 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.

https://kirkmanandjourdain.com/dj6kt8m1jl 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.

source The https://www.anonpr.net/nhaa6mla15s 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.

go Cover Front Matter 1. Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus Part I. Clinical Prediction Modeling 2. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I—Introduction and General Principles 3. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II—Generalization and Overfitting 4. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III—Model Evaluation and Other Points of Significance 5. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV—A Practical Approach to Binary Classification Problems 6. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V—A Practical Approach to Regression Problems 7. Foundations of Feature Selection in Clinical Prediction Modeling 8. Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience 9. A Discussion of Machine Learning Approaches for Clinical Prediction Modeling 10. Foundations of Bayesian Learning in Clinical Neuroscience 11. Introduction to Deep Learning in Clinical Neuroscience 12. Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications 13. Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators 14. Updating Clinical Prediction Models: An Illustrative Case Study 15. Is My Clinical Prediction Model Clinically Useful? A Primer on Decision Curve Analysis Part II. Neuroimaging 16. Introduction to Machine Learning in Neuroimaging 17. Machine Learning Algorithms in Neuroimaging: An Overview 18. Machine Learning-Based Radiomics in Neuro-Oncology 19. Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images 20. Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python 21. Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging 22. Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning 23. Tackling the Complexity of Lesion-Symptoms Mapping: How to Bridge the Gap Between Data Scientists and Clinicians? Part III. Natural Language Processing and Time Series Analysis 24. Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete 25. Foundations of Time Series Analysis 26. Overview of Algorithms for Natural Language Processing and Time Series Analyses Part IV. Ethical and Historical Aspects of Machine Learning in Medicine 27. A Brief History of Machine Learning in Neurosurgery 28. Machine Learning and Ethics 29. The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI 30. Predictive Analytics in Clinical Practice: Advantages and Disadvantages Part V. Clinical Applications of Machine Learning in Clinical Neuroscience 31. Big Data in the Clinical Neurosciences 32. Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review 33. Machine Learning in Pituitary Surgery 34. At the Pulse of Time: Machine Vision in Retinal Videos 35. Artificial Intelligence in Adult Spinal Deformity 36. Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction 37. Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery 38. Radiomic Features Associated with Extent of Resection in Glioma Surgery 39. Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer’s Disease, and Schizophrenia