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 Batista, R. and Bults, R.G.A. and Akker op den, H. and Widya, I. and Hermens, H. and Huis in 't Veld, R. and Tonis, T. and Vollenbroek-Hutten, M. (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.

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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 Item
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Link to this item:http://purl.utwente.nl/publications/77943
Conference URL:http://www.liaad.up.pt/~pprodrigues/lmds11/
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