某地区基层医疗机构医院感染管理现状

  目的  为解决医院感染病例“上报难”问题，初步建立神经网络模型在医院感染病例预警中的应用。  方法  通过神经网络与决策树分类器相结合，2017年3月1 — 31日通过对某三甲医院特定时间内抽取的4 911例感染病例的信息进行分析，得到一个由训练过后神经网络生成的规则算法，再由该方法对另一个时段内患者信息进行预测，并将预测结果与实际结果进行对比，以寻求一种针对医院感染信息系统最佳的数据分析核心算法。  结果  在模型的拟合程度上，classification tree模型优于neural network模型，同时2者大大优于logistics模型；在预测结果的精准度上，classification tree模型亦优于logistics模型；将coarsetree和neuralnet模型的结果进行交叉互补时，可明显减少假阴性病例数。  结论  神经网络与决策树分类器相结合对结果预测的精准性远远高于传统的logistic模型。

Possibilistic LVQ neural network-an application to childhood autism grading

  Objective  To explore the application of neural network model (NNM) in early warning of nosocomial infection (NI) for effective control of NI.  Methods  We extrated data on 4 911 inpatients with infections (139 NI and 4 776 non-NI) in a terciary grade A hospital during March 2017. A algorithm formula was established using NNM combined with decision tree classifier after training based on the data collected. Then the established algorithm formula was atopted to predicate the occurence of infection inpatients in the hospital in a specific duration and compared the predictions to those of actual occurences to develop an optimal core algorithm for analysis of data from hospital infection information system.  Results  For the models established, the fitting of classification tree was better than that of NNM and both the fitting of classification tree and NNM were vastly superior to that of logistic model. The predictive accuracy of classification tree model was better than that of logisitics model. The number of false negative prediction was obviously decreased with cross-complementing of coarsetree model and neuralnet model.  Conclusion  In predication of NI occurence, the predictive accuracy of neural network model combined with classification tree model is obviously hgither than taht of conventional logistics model.