Machine learning (ML) techniques are used as predictive models for many applications including those in the field of biomedicine. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often not properly understood. Most biomedical data are categorized into structured, semi-structured, and unstructured types with very high volume. The volume and complexity of these data present new opportunities, but also pose new challenges. Automated algorithms that extract meaningful patterns could lead to actionable knowledge and change how we develop treatments, categorize patients, or study diseases, all within privacy-critical environments. This book addresses the issues described to predict and model biomedical data mining and analysis. The book has been organized into 15 chapters.
