Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Expert Systems with Applications: An International Journal
Editorial: Hybrid learning machines
Neurocomputing
A Scalable, Self-Adaptive Architecture for Remote Patient Monitoring
ISORCW '10 Proceedings of the 2010 13th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops
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Advancements in the development of medical apparatuses and in the ubiquitous availability of data networks make it possible to equip more patients with telemonitoring devices. As a consequence, interpreting the collected data becomes an increasing challenge. Medical observations traditionally have been interpreted in two competing ways: using established theories in a rule-based manner, and statistically (possibly leading to new theories). In this paper, we study a hybrid approach that allows both evaluation of a fixed set of rules as well as machine learning to coexist. We reason that this hybrid approach helps to increase the level of trust that doctors have in our system, by reducing the risk of false negatives.