Machine Learning Techniques for Decision Support in Anesthesia

  • Authors:

  • Olivier Caelen;Gianluca Bontempi;Luc Barvais

  • Affiliations:

  • Machine Learning Group, Département d'Informatique, Université Libre de Bruxelles, Bruxelles, Belgium;Machine Learning Group, Département d'Informatique, Université Libre de Bruxelles, Bruxelles, Belgium;Service d'Anesthésiologie-Réanimation, Faculté de Médecine, Université Libre de Bruxelles, Bruxelles, Belgium

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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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.