Bolinger R E, Hopfensperger K J, Preston D F
Department of Medicine, Kansas University Medical Center, Kansas City 66103.
Proc Annu Symp Comput Appl Med Care. 1991:310-4.
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.
甲状腺疾病的筛查实验室检测通常包括血清甲状腺素（T4）、促甲状腺激素（TSH）水平以及作为T4与血清结合指标的三碘甲状腺原氨酸树脂结合（T3）。一个以上述值作为输入的神经网络在训练过程中无法收敛，以识别六种常见甲状腺功能状态的输出诊断结果。当提供结合蛋白（TBG）数据时，该网络很容易收敛。由于甲状腺素结合可以从T4和T3之间的关系大致估算出来，因此从每个T4/T3输入集计算出一个反映结合情况的虚拟输入节点，并将其用作额外输入。有了这个补充，系统能够轻松训练并从训练集中准确诊断。
1）定量实验室数据可用于诊断网络的输入神经元。2）使用虚拟TBG神经元，网络的训练和诊断准确性比省略TBG数据或使用实际TBG值更高效。
