Department of Family Medicine, Medical School Osijek, University of Osijek, Osijek, Croatia.
J Biomed Inform. 2010 Oct;43(5):774-81. doi: 10.1016/j.jbi.2010.04.011. Epub 2010 May 6.
The major challenge in influenza vaccination is to predict vaccine efficacy. The purpose of this study was to design a model to enable successful prediction of the outcome of influenza vaccination based on real historical medical data. A non-linear neural network approach was used, and its performance compared to logistic regression. The three neural network algorithms were tested: multilayer perceptron, radial basis and probabilistic in conjunction with parameter optimization and regularization techniques in order to create an influenza vaccination model that could be used for prediction purposes in the medical practice of primary health care physicians, where the vaccine is usually dispensed. The selection of input variables was based on a model of the vaccine strain which has frequently been changed and on which a poor influenza vaccine response is expected. The performance of models was measured by the average hit rate of negative and positive vaccine outcome. In order to test the generalization ability of the models, a 10-fold cross-validation procedure revealed that the model obtained by multilayer perceptron produced the highest average hit rate among neural network algorithms, and also outperformed the logistic regression model with regard to sensitivity and specificity. Sensitivity analysis was performed on the best model and the importance of input variables was discussed. Further research should focus on improving the performance of the model by combining neural networks with other intelligent methods in this field.
流感疫苗接种的主要挑战在于预测疫苗的效果。本研究旨在设计一种模型，以便能够根据真实的历史医学数据成功预测流感疫苗接种的结果。使用了一种非线性神经网络方法，并将其性能与逻辑回归进行了比较。测试了三种神经网络算法：多层感知器、径向基函数和概率神经网络，并结合参数优化和正则化技术，以创建一种可用于初级保健医生医疗实践中预测目的的流感疫苗接种模型，因为通常在这些地方接种疫苗。输入变量的选择基于疫苗株的模型，该模型经常发生变化，预计对流感疫苗的反应不佳。通过负面和正面疫苗结果的平均命中率来衡量模型的性能。为了测试模型的泛化能力，通过 10 折交叉验证程序发现，多层感知器获得的模型在神经网络算法中产生了最高的平均命中率，并且在灵敏度和特异性方面也优于逻辑回归模型。对最佳模型进行了敏感性分析，并讨论了输入变量的重要性。进一步的研究应集中于通过将神经网络与该领域的其他智能方法相结合来提高模型的性能。
