NDLI: Boosting-based discovery of multi-component physiological indicators: applications to express diagnostics and personalized treatment optimization

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Boosting-based discovery of multi-component physiological indicators: applications to express diagnostics and personalized treatment optimization

Content Provider	ACM Digital Library
Author	Ulyanova, Olga N. Koepke, Mark E. Gavrishchaka, Valeriy V.
Abstract	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.
Starting Page	790
Ending Page	799
Page Count	10
File Format	PDF
ISBN	9781450300308
DOI	10.1145/1882992.1883111
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-11-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Model combination Physiological modeling Nonlinear dynamics Rr time series Therapy optimization Personalized and sport medicine Boosting Express diagnostics Ensemble learning algorithms Heart rate variability
Content Type	Text
Resource Type	Article

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