ArticleBMC medical informatics and decision making2018
Time series model for forecasting the number of new admission inpatients.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 132 citations in OpenAlex.
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- Comparison of SES method and SARIMA model in predicting the number of admissions in the department of neurology.Scientific reports · 2025Article
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- Time series forecasting of red blood cell demand in hematology patients using SARIMA and exponential smoothing models: a retrospective analysis in a Chinese tertiary hospital.Frontiers in medicine · 2025Article
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- Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province.BMC public health · 2023Article
- A multi-granular stacked regression for forecasting long-term demand in Emergency Departments.BMC medical informatics and decision making · 2023Article
- Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya.PLOS digital health · 2023Article
- Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China.BMC infectious diseases · 2022Article
- Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach.BMC medical informatics and decision making · 2022Article
- Forecasting the rate of hand injuries in Singapore.Journal of occupational medicine and toxicology (London, England) · 2022Article
- Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction.Environmental science and pollution research international · 2022Article
- Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022.Infection and drug resistance · 2022Article
- HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models.Medicine · 2021Article
- Short-term forecasting of the COVID-19 outbreak in India.International health · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding.
methodsWe used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016.
resultsFor the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage.
conclusionsHybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.