جستجوی مقالات مرتبط با کلیدواژه "exponential smoothing" در نشریات گروه "پزشکی"

Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.

Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).

The optimum ES model was Brown’s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.

The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.

Understanding the prevalence of schizophrenia has important implications for both health service planning and risk factor epidemiology. The aims of this study are to systematically identify and collate studies describing the prevalence of schizophrenia, to summarize the findings of these studies, and to explore selected factors that may influence prevalence estimates.
This historical cohort study was done on schizophrenia patients in Farshchian psychiatric hospital from April 2008 to April 2016. To analyze the data, the Holt-Winters Exponential Smoothing (HWES) method was applied. All the analyses were done by R.3.2.3. Software using the packages “forecast” and “tseries”. The statistical significant level was assumed as 0.05.
Our investigation show that a constant frequency of Schizophrenia incidence happens every month from August 2008 to February 2015 while a considerable increase occurs in March 2015. The high frequency of Schizophrenia incidence remains constant to the end of 2015 and a decrease is shown in 2016. Also, data demonstrate the development of Schizophrenia in the next 24 months with 95% confidence interval.
Our study showed that a significant increase happens in the frequency of Schizophrenia from 2016. Although the development is not constant and the same for all months, the amount of increase is considerably high comparing to before 2016.

To report a scientific forecast of the number of published dental articles in the next 20 years.
On October 12, 2016, to find all dental articles, PubMed was searched via the query “1800/1/1”[PDAT]: “2015/12/31”[PDAT] AND jsubsetd [text]. Relevant limitations were applied to find dental clinical trials, review articles, and free full-text dental articles. Consequently, all PubMed records were exported to a CSV file. To forecast the future dental research output using existing time-based data, the Exponential Triple Smoothing algorithm was used, which is an advanced machine learning algorithm. Data were analyzed by Microsoft Office Excel 2016.
Seventy-five (1940–2015) years of human attempts to publish dental articles were explored and 572490 records were found, from which 27244 (4.75%) articles were free full-text, 19238 (3.36%) were clinical trials, and 31853 (5.56%) were reviews. Researchers will publish 19195 dental articles in 2036, among which 917 (4.77%) articles will be clinical trials, 1474 (7.67%) will be review articles, and 5482 (28.55%) will be free full-text articles.
Changes may be because of the quantity of research funds. The number of all types of dental articles will increase with an acceptable rate over the next 20 years. Of more interest, the number of free full-text articles will grow more rapidly than other article types.