Using model checking for critiquing based on clinical guidelines

  • Authors:

  • Perry Groot;Arjen Hommersom;Peter J. F. Lucas;Robbert-Jan Merk;Annette ten Teije;Frank van Harmelen;Radu Serban

  • Affiliations:

  • Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands;Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands;Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands;Division of Mathematics and Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands;Division of Mathematics and Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands;Division of Mathematics and Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands;Division of Mathematics and Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2009

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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.