ABSTRACT
Objective To compare the prediction effect of combined model and single model in HFRS incidence fitting and prediction, and to provide a reference for optimizing HFRS prediction model. Methods The province with the highest incidence in China (Heilongjiang Province) in recent years was selected as the research site. The monthly incidence data of HFRS in Heilongjiang Province from 2004 to 2017 were collected. The data from 2004 to 2016 was used as training data, and the data from January to December 2017 was used as test data. The training data was used to train SARIMA , ETS and NNAR models, respectively. The reciprocal variance method and particle swarm optimization algorithm (PSO) were used to calculate the model coefficients of SARIMA, ETS and NNAR, respectively, to construct combined model A and combined model B. The established models were used to predict the incidence of HFRS from January to December 2017. The fitted and predicted values of the five models were compared with the training data and test data, respectively. Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Standard Deviation (RMSE), and Mean Error Rate (MER) were used to evaluate the model fitting and prediction effects. Results The optimal SARIMA model was SARIMA(1,0,2)(2,1,1)12. The optimal ETS model was ETS(M, N, M), and the smoothing parameter =0.738，=1*10. The optimal NNAR model was NNAR(13,1,7)12. The residuals of the three single models were white noise (P>0.05). The expression of combined model A was ŷ=0.134*ySARIMA+0.162*yETS+0.704*yNNAR; the expression of combined model B was ŷ=0.246*ySARIMA+0.435*yETS+0.319*yNNAR. The MAPE, MAE, RMSE, and MER fitted by SARIMA, ETS, NNAR, combined model A and combined model B were 24.10%, 0.11, 0.17, 23.29%; 17.14%, 0.08, 0.14, 17.96%; 6.33%, 0.02, 0.03, 4.25%; 9.03%, 0.03, 0.05, 7.51%; 13.16%, 0.06, 0.09, 12.33%, respectively. The MAPE, MAE, RMSE, and MER predicted by the five models were 18.70%, 0.05, 0.06, 19.62%; 23.83%, 0.06, 0.07, 24.49%; 28.30%, 0.07, 0.10, 29.21%; 21.69%, 0.06, 0.08, 22.63%; 17.39%, 0.05, 0.07, 18.76%, respectively. Conclusion The fitting and prediction effects of the combined models are better than the single models. The combined model based on PSO to calculate the weight of the single model is the optimal model.
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ObjectiveTo establish and evaluate a chronic obstructive pulmonary disease （COPD） model with lung-spleen qi deficiency. MethodA rat model mimicking COPD with lung-spleen qi deficiency was established by the combination of cigarette smoking and intratracheal instillation of lipopolysaccharide （LPS） along with gavage of Sennae Folium infusion. Forty male SPF-grade SD rats were randomly assigned to blank， model， and low- （L-FXY）， medium- （M-FXY）， and high-dose （H-FXY） Sennae Folium infusion groups. Other groups except the blank group were exposed to daily cigarette smoke， with LPS administrated via intratracheal instillation on the 1st and 14th days. On the 28th day of modeling， the L-FXY， M-FXY， and H-FXY groups were administrated with Sennae Folium infusion at 5， 10， and 20 g·kg-1， respectively， and at 4 ℃ for three weeks. The modeling lasted for 49 days. The general conditions （body mass， food intake， fecal water content， and anal temperature） and behaviors （grip strength test and tail suspension test） of rats in different groups were examined. The lung function， lung histopathology， D-xylose， amylase， and gastrin levels in the serum， interleukin（IL）-1β and IL-6 levels in the alveolar lavage fluid， levels of T-lymphocyte subsets （CD4+， CD8+， and CD4+/CD8+） in the peripheral blood， and thymus and spleen indices were measured. ResultTwo rats died in the H-FXY group. Compared with the blank group， both the M-FXY and H-FXY groups exhibited reduced body mass and food intake （P<0.01） and increased fecal water content （P<0.01）. The anal temperature in the H-FXY group was lower than that in the blank group （P<0.01）. The grip strength decreased in the modeling groups compared with the blank group （P<0.01）， and the duration of immobility in the tail suspension test increased in the M-FXY and H-FXY groups （P<0.05， P<0.01）. Compared with the blank group， the modeling groups showed reduced 0.3 second forced expiratory volume （FEV0.3）， FEV0.3/forced vital capacity （FVC）（P<0.01）， thickening of bronchial walls， proliferation of goblet cells， and the presence of emphysematous changes. In terms of gastrointestinal function， the M-FXY and H-FXY groups had lower levels of D-xylose， gastrin， and α-amylase than the blank group （P<0.01）. Regarding the immune and inflammatory indices， the M-FXY and H-FXY groups showed lower thymus and spleen indices than the blank group （P<0.01）. Compared with the blank group， the modeling groups presented lowered CD4+ level （P<0.01） and CD4+/CD8+ ratio （P<0.05， P<0.01） in the peripheral blood and elevated levels of IL-1β and IL-6 in the alveolar lavage fluid （P<0.01） than the blank group. ConclusionA model of COPD with lung-spleen Qi deficiency was established through the combination of daily cigarette smoke， intratracheal instillation with LPS， and gavage of Sennae Folium infusion. The comprehensive evaluation results suggested medium-dose （10 g·kg-1） Sennae Folium infusion for gavage during the modeling of COPD with lung-spleen Qi deficiency.
ABSTRACT
Objective:To study the effectiveness of different time series models in the prediction of financial data in public hospitals,with the aim of obtaining a more reliable counterfactual in health policy evaluation. Methods:ARI-MA model,BP neural network and their combination were used for the estimation and prediction of drug revenue and medical service revenue based on a dataset for the period from November,2011 to October,2016 for hospital X before and after Nanjing medical pricing reform. Root mean square error (RMSE) was used to estimate the model accuracy. Results:RMSE of drug revenue from the three models were 692.82,501.44 and 380.80,and of medical service were 184.04,215.63 and 168.65. The findings shows that the combination model was proved to be the most efficient one a-mong the three. The combined model was used to calculate the net loss of drug revenue which was estimated to be 120, 440 million,and the net increase of medical service was estimated to be 185,326 million after the reform,which was 1. 539 times of the drug loss. Conclusions:The revenue data of public hospitals are usually complex with a both linear and non-linear trend. The combination model of ARIMA and BP neural network could solve the problem for once with an acceptable accuracy. However,ARIMA model is simpler to operate as compared to other two models, and also more consistent with the forecasting trend,therefore ARIMA is also recommended in the evaluation for health policies.