Abstract
In this paper we introduce clinical reasoning automata to model states and transitions about different cognitive processes that occur during a clinical reasoning activity. A state of the automaton represents a particular process in a complex patient diagnosis using influence diagrams encoding clinical knowledge about the case. Transitions model switch between diagnosis cognitive processes, such as collecting evidences, formulating hypothesis or explicitly asking for assistance at a given point during the reasoning process. That way, we can efficiently model tutoring feedback hints for clinical reasoning learning that are based not only on the clinical knowledge, but also on the sequencing of the tutoring processes.
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Kabanza, F., Bisson, G. (2005). Clinical Reasoning Learning with Simulated Patients. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_51
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DOI: https://doi.org/10.1007/11527770_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
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