Model-based guideline development for symptom-based indications

This work proposes a model-based guideline development method for complex diagnostic and treatment policies, for instance for symptom-based indications. An influence diagram has advantages for the development of outcome-based guidelines, because it provides an explicit link between the policies and these outcomes. A Bayesian network have a strong inference capability, suitable for complex symptom-based medical diagnostic modelling incorporating many diseases. However, neither supports temporal sequences. The modelling of symptom-based indications for guideline distillation can be supported by imposing a strict structure and a strict order. A further extension is the user interface for evaluation and ranking of the probabilities, policies, and the resulting outcomes. Instead of seeking for the best solution, the guideline is distilled by ranking these outcomes with respect to the treatment, anatomic region and pathology, and the generally expected outcome if treated properly. The proposed guideline distillation method is expected to improve the development, and therefore the quality of the guidelines.