Reibnegger G, Weiss G, Werner-Felmayer G, Judmaier G, Wachter H
Institute for Medical Chemistry and Biochemistry, University of Innsbruck, Austria.
Proc Natl Acad Sci U S A. 1991 Dec 15;88(24):11426-30. doi: 10.1073/pnas.88.24.11426.
Successful applications of neural network architecture have been described in various fields of science and technology. We have applied one such technique, error back-propagation, to a medical classification problem stemming from clinical chemistry, and we have compared the performance of two different neural networks with results obtained by conventional linear discriminant analysis or by the technique of classification and regression trees. The results obtained by the various models were tested for robustness by jackknife validation ("leave n out" method). Compared with the two other techniques, neural networks show a unique ability to detect features hidden in the input data which are not explicitly formulated as input. Thus, neural network techniques appear promising in the field of clinical chemistry, and their application, particularly in situations with complex data structures, should be investigated with more emphasis.
神经网络架构已在科学技术的各个领域得到成功应用。我们将其中一种技术——误差反向传播应用于一个源自临床化学的医学分类问题，并将两种不同神经网络的性能与通过传统线性判别分析或分类与回归树技术获得的结果进行了比较。通过刀切法验证（“留一法”）对各种模型得到的结果进行了稳健性测试。与其他两种技术相比，神经网络具有独特的能力，能够检测隐藏在输入数据中未明确作为输入表述的特征。因此，神经网络技术在临床化学领域似乎很有前景，应更着重研究其应用，特别是在具有复杂数据结构的情况下。
