The EMG diagnosis--an interpretation based on partial information

There is a large difference between the prevalence of a given disease in the general population and in the population seen in the EMG lab. It can be argued that both prevalences are the correct choice as prior probabilities for the diseases. This paradox is resolved by recognizing that the EMG diagnosis is only based on the information provided by the EMG examination and thus only represents a partial view of the patient. We propose a solution summarizing the set of findings, signs and symptoms, lab results etc., that led to the referral of the patient for an EMG examination. This information is described by stochastic variables called FIDL factors (Found In Doctor's Lab). The approach is tested on the EMG expert system MUNIN with 30 previously evaluated cases. The results show that this solution improves the specificity of the diagnosis, without affecting the sensitivity.