Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy

We successfully built a generic model suitable for all infectious diseases and patient profiles. This model includes both preferences and necessary features. It allowed the detection of 106 candidate inconsistencies which were analyzed by a medical expert. 55 inconsistencies were validated. We showed that therapeutic strategies of guidelines in antibiotherapy can be formalized by a preference model. In conclusion, we proposed an original approach, based on preferences, for modeling clinical guidelines. This model could be used in future clinical decision support systems for helping physicians to prescribe antibiotics.