Abstract
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.
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Heinze, T., Wierschke, R., Schacht, A., von Löwis, M. (2011). A Hybrid Artificial Intelligence System for Assistance in Remote Monitoring of Heart Patients. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_50
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DOI: https://doi.org/10.1007/978-3-642-21222-2_50
Publisher Name: Springer, Berlin, Heidelberg
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