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20458 Interpreting streaming biosignals: in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support
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Jones, V.M. and Mendes Batista, R.J. and Bults, R.G.A. and op den Akker, H. and Widya, I.A. and Hermens, H.J. and Huis in 't Veld, M.H.A. and Tönis, T.M. and Vollenbroek-Hutten, M.M.R. (2011) Interpreting streaming biosignals: in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support. In: Workshop on Learning from Medical Data Streams, LEMEDS, co-located with 13th Conference on Artificial Intelligence in Medicine, AIME 2011, 6 July 2011, Bled, Slovenia. AIME 2011. ISBN not assigned

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Abstract

We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge sources. We illustrate with two prototype applications: the first is designed to detect epileptic seizures and support appropriate intervention. The
second is a decision support application aiding weight management; the goal is to promote health and prevent chronic illnesses associated with overweight/obesity. We begin to explore extending these and other m-health applications with generic AI-based decision support and machine learning. Monitoring success of different behavioural change strategies could provide a basis for
machine learning, enabling adaptive clinical decision support by personalising and adapting strategies to individuals and their changing needs. Data mining
applied to BAN data aggregated from large numbers of patients opens up possibilities for discovery of new clinical knowledge.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-BSS: Biomedical Signals and Systems
Research Program:CTIT-ASSIST: Applied Science of Services for Information Society Technologies
Research Project:FOVEA: Food Valley Eating Administrator/Advisor
Additional Information:Co-located with 13th Conference on Artificial Intelligence in Medicine (AIME'11), July 2-6, 2011, Bled, Slovenia. http://www.liaad.up.pt/~pprodrigues/lmds11/
Uncontrolled Keywords:Ambulant monitoring, patient monitoring, Body Area Networks, Clinical Decision Support, machine learning, medical data streams
ID Code:20458
Status:Published
Deposited On:31 August 2011
Refereed:Yes
International:Yes
More Information:statisticsmetis

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