Predictive Modeling in Biomedical Data Mining and Analysis

Predictive Modeling in Biomedical Data Mining and Analysis presents readers with the major technical advancements and research findings in the field of Machine Learning in biomedical image and data analysis. The book examines the recent technologies and studies that have reached the practical level and have become available in preclinical and clinical practices in computational intelligence. The authors present leading-edte research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis.Machine Learning techniques are used as predictive models for many types of application, including biomedical applications. 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 ill-understood. Most biomedical data are categorized as structured, semi-structured and unstructured types, with very high volume. The volume and complexity of these datasets present new opportunities, and also pose new challenges. Automated algorithms that extract meaningful patterns can lead to actionable knowledge and change how we develop treatments, categorize patients or study diseases, all within privacy-critical environments.Hence, Machine Learning techniques are particularly well suited to solve problems of Data Mining and analysis. Across biomedical fields, ‘off-the-shelf’ implementations of these algorithms have produced comparable or higher accuracy than previous best-in-class methods that required years of extensive customization, and specialized implementations are now being used at industrial scales. The application of Machine Learning is spreading to a variety of biomedical problems—automatic image segmentation, image classification, disease classification, fundamental biological processes and treatment to the response of patients.Machine learning has yet to revolutionize biomedical engineering, or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that machine learning methods will provide a valuable means for speeding up or aiding human investigation. Though progress can be made linking a specific neural network’s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, in Predictive Modeling in Biomedical Data Mining and Analysis, the authors present methods for machine learning to enable changes at both bench and bedside with the potential to transform several areas of biomedical data mining and analysis. Includes predictive modelling algorithms for both Supervised Learning and Unsupervised Learning for medical diagnosis, data summarization, and pattern identification Offers a complete coverage of predictive modelling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models, and deep neural networks Provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications