Prediction of women and Children's hospital outpatient numbers based on the autoregressive integrated moving average model.

Yan Lin, Chaomin Wan, Sha Li, Shina Xie, Yujing Gan, YuanHu Lu

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Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.Frontiers in public health · 2024
Article
The forecasting of pediatric asthma clinic visits: A comparative analysis of time-series models under varying training set sizes.Digital health
Article

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5 · Who and what money

Authors and funding

6 authors.

Yan LinOutpatient Department, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610091, China.

Chaomin WanDepartment of Paediatrics, West China Second Hospital, Sichuan University, No. 20, 3rd Section of Renmin South Road, Chengdu 610041, PR China.

Sha LiDepartment of Pediatric Rheumatology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610091, China.

Shina XieSichaun Development Big Data Industry Investment Co., Ltd., Chengdu 610041, China.

Yujing GanSichuan Development Holding Co., LTD Full-time Director, Chengdu 610041, China.

YuanHu LuDepartment of Information, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610091, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the predictive value of the autoregressive integrated moving average (ARIMA) product seasonal model for the daily outpatient volume of paediatric internal medicine departments in hospitals. Methods: The daily outpatient volume of paediatric internal medicine recorded by the hospital information system of the Chengdu Women's and Children's Central Hospital from 1 January 2011 to 31 December 2020 was collected. Using the data from 1 January 2011 to 31 December 2019, the seasonal summation ARIMA model of the time product was established by fitting the tseries program in the R-3.6.3 software. The monthly outpatient volume from January to December 2020 was predicted, and the prediction effect was evaluated according to the mean absolute percentage error (MAPE) between the predicted value and the actual value. Results: The outpatient volume of paediatric internal medicine in the hospital from 2011 to 2019 showed an upward trend, with obvious seasonal fluctuations. The optimal model was the ARIMA model ([3,4], 1,2) × (1,1,0) 12, with an Akaike information criterion of 3116.656 and a Bayesian information criterion of 3217.412. The model's residual was a white noise sequence (x Conclusion: The ARIMA multiplicative seasonal model established in this study is suitable for the short-term prediction of the outpatient volume.

Indexed as

ARIMA modelEffect evaluationForecastMonthly outpatient volumePaediatric internal medicine

Identifiers

PMID37089366

DOI10.1016/j.heliyon.2023.e14845

PMCPMC10114184

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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.