Abstract
The growing availability of measurement devices in the operating room enables the collection of a huge amount of data about the state of the patient and the doctors’ practice during a surgical operation. This paper explores the possibilities of generating, from these data, decision support rules in order to support the daily anesthesia procedures. In particular, we focus on machine learning techniques to design a decision support tool. The preliminary tests in a simulation setting are promising and show the role of computational intelligence techniques in extracting useful information for anesthesiologists.
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© 2007 Springer-Verlag Berlin Heidelberg
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Caelen, O., Bontempi, G., Barvais, L. (2007). Machine Learning Techniques for Decision Support in Anesthesia. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_20
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DOI: https://doi.org/10.1007/978-3-540-73599-1_20
Publisher Name: Springer, Berlin, Heidelberg
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