Non-linear Analysis of Medical and Pharmaceutical Data using Neural Network and Generalized Additive Model

我们尝试采用几种非参数数据分析方法，如GAM、MARS、决策树、MART、层次人工神经网络（HANN）和Livingstone型人工神经网络（LANN），对医学和制药科学领域的数据进行分析。本文首先对Freedman等人提出的两种非参数非线性回归方法MARS和MART进行了比较，使用理想的人工数据。MARS表现出令人惊讶的良好拟合和预测性能; MART也表现出相当好的性能。结果表明MART是一种鲁棒的数据挖掘方法。因此，我们认为，对于噪声较小的数据应采用MARS方法，对于噪声较大的数据应采用MART方法，并将这些方法应用于流行病学数据集。例如，MARS a ...更多信息 并将MART方法应用于雌激素的药物流行病学研究，认为雌激素可能与子宫内膜癌的发生有关。结果表明，雌激素的活性形式与癌症的发生有关。虽然我们可以从雌激素的药物流行病学研究中获得一定的信息，但在非参数模型中对各个预测变量的显著性进行检验并不容易。因此，我们引入了扩展移位检验方法，该方法是我们小组修订的，以检验预测变量的显著性。这些方法也应用于食管癌发病与饮酒关系的临床流行病学研究。此外，我们将LANN应用于气液色谱保留值的非线性主成分分析。与线性主成分分析方法相比，该方法的分类结果更加清晰。少

We have tried to adopt the several nonparametric data analysis methods, such as GAM, MARS, decision tree, MART, hierarchical artificial neural networks (HANN), and Livingstone type artificial neural networks (LANN), to the data in the field of medical and pharmaceutical sciences. And the reasonable results of regression and principal component analyses were obtained.First, we compared the two nonparametric nonlinear regression methods, MARS and MART, which were developed by Freedman et al., using ideal artificial data. MARS showed a surprisingly good fitting and prediction performances ; MART also showed quite good performances. And the results indicate that MART is a robust data mining method. Thus, we resulted that the MARS should be adopted for the data containing little noise and that the MART should be adopted for the data analyses which can be affected by noise.Then, we applied these methods including the GAM and the HANN to some epidemiological data sets. For example, the MARS a … More nd the MART methods were applied to the pharmacoepidemiological study of estrogen, which might be related to the onset of endometrial cancer. The results show that the active form of estrogen is related to the onset of the cancer. Although we could get a certain information of the pharmacoepidemiological study of estrogen, it was not so easy to test the significance of each prediction variables used in such nonparametric models. Thus, we introduced the extended shift test method, which was revised by our group, in order to test the significance of prediction variables. These methods were also applied to the clinical epidemiological study on the relation between onset of esophagus cancer and alcohol consumption. We confirmed the possibility that the alcohol consumption affect the esophagus cancer.In addition, we applied the LANN to the nonlinear principal component analyses of Gas-Liquid chromatographic retention data. The clearer classified result than the one obtained by linear PCA method could be obtained. Less