Research process, recap, and prediction of Chiari malformation based on bicentennial history of nomenclature and terms misuse.

There is an absent systematic analysis or review that has been conducted to clarify the topic of nomenclature history and terms misuse about Chiari malformations (CMs). We reviewed all reports on terms coined for CMs for rational use and provided their etymology and future development. All literature on the nomenclature of CMs was retrieved and extracted into core terms. Subsequently, keyword analysis, preceding and predicting (2023-2025) compound annual growth rate (CAGR) of each core term, was calculated using a mathematical formula and autoregressive integrated moving average model in Python. Totally 64,527 CM term usage was identified. Of these, 57 original terms were collected and then extracted into 24 core-terms. Seventeen terms have their own featured author keywords, while seven terms are homologous. The preceding CAGR of 24 terms showed significant growth in use for 18 terms, while 13, three, three, and five terms may show sustained growth, remain stable, decline, and rare in usage, respectively, in the future. Previously, owing to intricate nomenclature, Chiari terms were frequently misused, and numerous seemingly novel but worthless even improper terms have emerged. For a very basic neuropathological phenomenon tonsillar herniation by multiple etiology, a mechanism-based nosology seems to be more conducive to future communication than an umbrella eponym. However, a good nomenclature also should encapsulate all characteristics of this condition, but this is lacking in current CM research, as the pathophysiological mechanisms are not elucidated for the majority of CMs.

Research on the sustainable development of agricultural product supply chain in three northeast provinces in China.

Background: The sustainable development of the agricultural product supply chain (APSC) is the key to protecting public health. Methods: This paper explores the sustainable development status of the APSC in three northeast provinces of China from 2007 to 2020 and the development trend in the next 5 years by using the entropy weight-matter-element extension model (MEEM) and autoregressive integrated moving average model (ARIMA), taking into account the background of relatively backward development and the high proportion of agricultural output in these three provinces. Results: According to the research results, the sustainable development of the APSC in Jilin Province is relatively stable, Heilongjiang Province has made considerable progress in the sustainable development of the APSC in recent years, while Liaoning Province has shown a significant downward trend in recent years in the sustainable development of the APSC, despite a strong development momentum in previous years. Conclusions: The findings of this paper can be applied to the governance of APSC in other rural areas with uneven development. The assessment also provides guidance on the quality and safety of agricultural products and public health, and raises the awareness of policymakers on the importance of the APSC.

Prediction of nosocomial infection incidence in the Department of Critical Care Medicine of Guizhou Province with a time series model.

BACKGROUND: The Department of Critical Care Medicine has the highest risk of nosocomial infection. This study used an autoregressive integrated moving average (ARIMA) model to simulate the prevalence of nosocomial infections in the Department of Critical Care Medicine of Guizhou Province. We also provided a policy basis for the prevention and control of hospital infection in the Department of Critical Care Medicine of Guizhou Province. METHODS: The data of ventilator-associated pneumonia, vascular catheter-related bloodstream infections, and urinary tract intubation-related urinary tract infections in nine tertiary A comprehensive treatment hospitals in Guizhou province from January 2014 to December 2019 were collected. The ARIMA time series model was used to evaluate the model fitting and prediction effects. RESULTS: After comparison, in the Department of Critical Care Medicine of Guizhou Province, the unsurpassed model of ventilator-associated pneumonia was the ARIMA (0,1,1) model, with a residual Ljuing-Box Q test result of Q=10.832 (P=0.865), suggesting it is a white noise sequence and its simulation and prediction effects are beneficial. The best model of vascular catheter-related bloodstream infection was the ARIMA (0,0,1) model, with a residual Ljuing-Box Q test result of Q=14.914 (P=0.602). These results suggest that it is a white noise sequence, and its simulation and prediction effects are sufficient. The optimal model of urinary tract intubation-related urinary tract infection is ARIMA (1,0,0), and the residual Ljuing-Box Q test result is Q=15.042 (P=0.592), suggesting it is a white noise sequence with an accurate simulation and prediction effect. CONCLUSIONS: The ARIMA model can accurately simulate and predict nosocomial infection incidence rate in the Department of Critical Care Medicine of Guizhou Province, and can provide a reference for the prevention and control of nosocomial infections.

Short and Long term predictions of Hospital emergency department attendances.

OBJECTIVE: Emergency departments in the United Kingdom (UK) experience significant difficulties in achieving the 95% NHS access standard due to unforeseen variations in patient flow. In order to maximize efficiency and minimize clinical risk, better forecasting of patient demand is necessary. The objective is therefore to create a tool that accurately predicts attendance at emergency departments to support optimal planning of human and physical resources. METHODS: Historical attendance data between Jan-2011 - December-2015 from four hospitals were used as a training set to develop and validate a forecasting model. To handle weekday variations, the data was first segmented into each weekday time series and a separate model for each weekday was performed. Seasonality testing was performed, followed by Box-Cox transformations. A modified heuristics based on a fuzzy time series model was then developed and compared with autoregressive integrated moving average and neural networks models using Harvey, Leybourne and Newbold (HLN) test. The time series models were tested in four emergency department sites to assess forecasting accuracy using the root mean square error and mean absolute percentage error. The models were tested for (i) short term prediction (four weeks ahead), using weekday time series; and (ii) long term predictions (four months ahead) using monthly time series. RESULTS: Data analysis revealed that presentations to emergency department and subsequent admissions to hospital were not a purely random process and therefore could be predicted with acceptable accuracy. Prediction accuracy improved as the forecast time intervals became wider (from daily to monthly). For each weekday time series modelling using fuzzy time series, for forecasting daily admissions, the mean absolute percentage error ranged from 2.63% to 4.72% while for monthly time series mean absolute percentage error varied from 2.01%-2.81%. For weekday time series, the mean absolute percentage error for autoregressive integrated moving average and neural network forecasting models ranged from 6.25% to 7.47% and 6.04%-7.42% respectively. The proposed fuzzy time series model proved to have statistically significant performance using Harvey, Leybourne and Newbold (HLN) test. This was explained by variations in attendances in different sites and weekdays. CONCLUSIONS: This paper described a heuristic-based fuzzy logic model for predicting emergency department attendances which could help resource allocation and reduce pressure on busy hospitals. Valid and reproducible prediction tools could be generated from these hospital data. The methodology had an acceptable accuracy over a relatively short time period, and could be used to assist better bed management, staffing and elective surgery scheduling. When compared to other prediction models usually applied for emergency department attendances prediction, the proposed heuristic model had better accuracy.

Application of ARIMA model in predicting monthly incidence of syphilis / 西安交通大学学报(医学版)

Objective To explore the value of the autoregressive integrated moving average model (ARIMA) applied to predict monthly incidence of syphilis so as to provide basis for prevention and control of syphilis . Methods Eviews 8 .0 was used to establish the ARIMA model based on the data of monthly incidence of syphilis in China from January 2009 to December 2015 .Then the data of the first half of 2016 were used to verify the predicted results .The predictions were evaluated by RMSE ,MAE ,MAPE and MRE models .Then the monthly incidence of syphilis in the second half of 2016 was predicted .Results The optimal model for the monthly incidence of syphilis from January 2009 to June 2016 was the model of ARIMA (2 ,1 ,1) × (0 ,1 ,1)12 ,its equation was (1 - B)(1 - B12 ) (1+0 .820 B)(1+0 .566 B2 ) x2t = (1+0 .365 B) (1+0 .897 B12 )εt ,its parameters are as follows :R2 =0 .832 ,RMSE=0 .181 ,MAE=0 .118 ,MAPE=5 .088 .The predicted monthly incidence values (10-5 ) of the second half of 2016 were 3 .124 ,3 .008 ,2 .906 ,2 .691 ,2 .714 ,and 2 .717 .Conclusion ARIMA model has a relatively good prediction precision .Therefore , it can make short-term prediction based on the evolution trend of monthly incidence of syphilis in China .

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

OBJECTIVE: To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. METHODS: The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. RESULTS: Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. CONCLUSIONS: The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model / 中国血吸虫病防治杂志

Objective To explore the effect of the autoregressive integrated moving average model?nonlinear auto?regressive neural network(ARIMA?NARNN)model on predicting schistosomiasis infection rates of population. Methods The ARIMA model,NARNN model and ARIMA?NARNN model were established based on monthly schistosomiasis infection rates from Janu?ary 2005 to February 2015 in Jiangsu Province,China. The fitting and prediction performances of the three models were com?pared. Results Compared to the ARIMA model and NARNN model,the mean square error(MSE),mean absolute error (MAE)and mean absolute percentage error(MAPE)of the ARIMA?NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4,respectively. Conclusion The ARIMA?NARNN model could effectively fit and predict schistosomiasis in?fection rates of population,which might have a great application value for the prevention and control of schistosomiasis.

Forecasting the number of human immunodeficiency virus infections in the korean population using the autoregressive integrated moving average model.

OBJECTIVES: From the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017. METHODS: HIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017. RESULTS: The fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985-2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889-1,036) and in 2017 was 1,111 (95% CI: 805-1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308-10,437) and in 2017 was14,724 (95% CI: 13,893-15,555) by ARIMA (1,2,3). CONCLUSION: Based on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000.