Application of a virtual neurode in a model thyroid diagnostic network.

UNLABELLED Screening laboratory tests for thyroid disease often include serum levels for thyroxine (T4), thyrotropic hormone (TSH), and triiodothyronine resin binding (T3) as a measure of T4 binding to serum. A neural network using the above values as input was unable to converge during training to identify an output diagnoses of six common thyroid functional states. When binding protein (TBG) data were supplied the network readily converged. Since thyroxine binding can be roughly estimated from a relationship between T4 and T3, a virtual input node reflecting the binding was calculated from each T4/T3 input set and used as additional input. With this addition, the system trained easily and accurately diagnosed from the training set. CONCLUSION 1) Quantitative laboratory data can be used in input neurodes in a diagnostic network 2) Training and diagnostic accuracy for the network is more efficient using the virtual TBG neurode than by either omitting TBG data or using actual TBG values.