The design and analysis of efficient learning algorithms
The design and analysis of efficient learning algorithms
Nonlinear time series analysis
Nonlinear time series analysis
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Machine Learning in Medical Applications
Machine Learning and Its Applications, Advanced Lectures
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
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ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 04
Proceedings of the 2004 international conference on Local Pattern Detection
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Increasing availability of multi-scale and multi-channel physiological data opens new horizons for quantitative modeling in medicine. However, practical limitations of existing approaches include both the low accuracy of the simplified analytical models and empirical expert-defined rules and the insufficient interpretability and stability of the pure data-driven models. Such challenges are typical for automated diagnostics from high-resolution image data and multi-channel temporal physiological information available in modern clinical settings. In addition, increasing number of portable and wearable systems for collection of physiological data outside medical facilities provide an opportunity for express and remote diagnostics as well as early detection of irregular and transient patterns caused by developing abnormalities or subtle initial effects of new treatments. However, quantitative modeling in such applications is even more challenging due to obvious limitations on the number of data channels, increased noise and non-stationary nature of considered tasks. Methods from nonlinear dynamics (NLD) are natural modeling tools for adaptive biological systems with multiple feedback loops and are capable of inferring essential dynamic properties from just one or a small number of data channels. However, most NLD indicators require long periods of data for stable calculation which significantly limits their practical value. Many of these challenges in biomedical modeling could be overcome by boosting and similar ensemble learning techniques that are capable of discovering robust multi-component meta-models from existing simplified models and other incomplete empirical knowledge. Here we describe an application of this approach to a practical system for express diagnostics and early detection of treatment responses from short beat-to-beat heart rate (RR) time series. The proposed system could play a key role in many applications relevant to e-healthcare, personalized medicine, express and remote web-enabled diagnostics, decision support systems, treatment optimization and others.