- 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
The book bridges the gap between computer 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.
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. These algorithms provide patients with objective information that may aid in risk-benefit discussion, help prevent adverse events, improve outcome, and patient safety overall.
traoperative applications of machine vision.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.
This work – authored by a wide array of experienced global machine learning groups – 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.
Contents Overview- Clinical Prediction Modeling
- Neuroimaging
- Natural Language Processing and Time Series Analysis
- Ethical and Historical Aspects of Machine Learning in Medicine
- Clinical Applications of Machine Learning in Clinical Neuroscience
