Expert Knowledge Acquisition and Machine learning

Having data sets regarding symptoms and diseases such that I use to observe the conditional distributions P(Disease X | Symptom A , Symptom H , Age >20 ) which I use for classification and diagnosis.

Now, a Domain expert comes and says – the data do not reflect reality, Disease X does not come really often with Symptom A. Or, Combination of Symptom H and A can also lead to Disease Y which never observed in the data.

What is the modern approach to combine the new knowledge that comes from domain experts to "tune" the classifiers / Augment the original data with the expert inputs? Without using just pure rule-base which won’t help the model generalizes.