Computer-interpretable implementations of clinical guidelines (CIGs) add knowledge that is outside the scope of the original guideline. This knowledge can customize CIGs to patients’ psycho-social context or address comorbidities that are common in the local population, potentially increasing standardization of care and patient compliance. We developed a two-layered contextual decision-model based on the PROforma CIG formalism that separates the primary knowledge of the original guideline from secondary arguments for or against specific recommendations. In this paper we show how constraint logic programming can be used to verify the layered model for two essential properties: (1) secondary arguments do not rule in recommendations that are ruled out in the original guideline, and (2) the CIG is complete in providing recommendation(s) for any combination of patient data items considered. We demonstrate our approach when applied to the asthma domain.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||16th Conference on Artificial Intelligence in Medicine, AIME 2017|
|Period||6/21/17 → 6/24/17|