Using model checking for critiquing based on clinical guidelines. Groot, P., Hommersom, A., Lucas, P., Merk, R., ten Teije , A., van Harmelen , F., & Serban, R. Artificial Intelligence in Medicine, 46(1):19–36, Elsevier, 2009. doi abstract bibtex Objective: Medical critiquing systems compare clinical actions performed by a physician with a predefined set of actions. 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, the critiquing system provides insight into the extent to which they are compatible. Methods and material: We propose a computational method 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. Results: We show how critiquing can be cast in terms of temporal logic, and what can be achieved by using model checking. Furthermore, a method is introduced for off-line computing relevant information which can be exploited during critiquing. The method has been applied to a clinical guideline of breast cancer in conjunction with breast cancer patient data. © 2008 Elsevier B.V. All rights reserved.
@article{d0f279ece3764d0db4671a586709882a,
title = "Using model checking for critiquing based on clinical guidelines",
abstract = "Objective: Medical critiquing systems compare clinical actions performed by a physician with a predefined set of actions. 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, the critiquing system provides insight into the extent to which they are compatible. Methods and material: We propose a computational method 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. Results: We show how critiquing can be cast in terms of temporal logic, and what can be achieved by using model checking. Furthermore, a method is introduced for off-line computing relevant information which can be exploited during critiquing. The method has been applied to a clinical guideline of breast cancer in conjunction with breast cancer patient data. © 2008 Elsevier B.V. All rights reserved.",
author = "P. Groot and A. Hommersom and P.F. Lucas and R. Merk and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H. and R.C. Serban",
year = "2009",
doi = "10.1016/j.artmed.2008.07.007",
volume = "46",
pages = "19--36",
journal = "Artificial Intelligence in Medicine",
issn = "0933-3657",
publisher = "Elsevier",
number = "1",
}
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