A critiquing approach to expert computer advice: Attending
A critiquing approach to expert computer advice: Attending
Survey of expert critiquing systems: practical and theoretical frontiers
Communications of the ACM
Critiquing: effective decision support in time-critical domains
Critiquing: effective decision support in time-critical domains
COMPOS'97 Revised Lectures from the International Symposium on Compositionality: The Significant Difference
A Task-Specific Ontology for the Application and Critiquing of Time-Oriented Clinical Guidelines
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Using Critiquing for Improving Medical Protocols: Harder than It Seems
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Verification of medical guidelines by model checking – a case study
SPIN'06 Proceedings of the 13th international conference on Model Checking Software
Authoring and verification of clinical guidelines: A model driven approach
Journal of Biomedical Informatics
ST-Audit: guideline-based automatic auditing of electronic patient records
Journal of Intelligent Information Systems
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Medical critiquing systems criticise clinical actions performed by a physician. In order to provide useful feedback, an important task is to find differences between the actual actions and a set of `ideal' actions as described by a clinical guideline. In case differences exist, insight to which extent they are compatible is provided by the critiquing system. We propose a methodology for such critiquing, where the ideal actions are given by a formal model of a clinical guideline, and where the actual actions are derived from real world patient data. We employ model checking to investigate whether a part of the actual treatment is consistent with the guideline. Furthermore, it is shown how critiquing can be cast in terms of temporal logic, and what can be achieved by using model checking. The methodology has been applied to a clinical guideline of breast cancer in conjunction with breast cancer patient data.