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Boosting-based Discovery of Multi-component Physiological Indicators: Applications to Express Diagnostics and Personalized Treatment Optimization by Valeriy V. Gavrishchaka; Mark E. Koepke; Olga N. Ulyanova is a scholarly article available to read on EtoBox.

What is Boosting-based Discovery of Multi-component Physiological Indicators: Applications to Express Diagnostics and Personalized Treatment Optimization about?

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 highresolution 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 lo

Author

Valeriy V. Gavrishchaka; Mark E. Koepke; Olga N. Ulyanova

Publisher

ACM

Published

2010

Language

EN