Application of SARIMA model in forecasting and analyzing inpatient cases of acute mountain sickness

BMC Public Health

Research

Han Song¹  Fangfang Yu²  Jianchao Liu³ 

[1] Department of Health Service, PLA General Hospital, No.28, Fuxing Road, 100853, Beijing, China;Department of Medical Innovation Research, PLA General Hospital, 100853, Beijing, China;Institute of Hospital Management, Department of Medical Innovation Research, PLA General Hospital, 100853, Beijing, China;

关键词: Acute Mountain Sickness;    Time series;    Seasonal Auto-Regressive Integrated Moving Average (SARIMA);    Prediction;   

DOI  :  10.1186/s12889-023-14994-4

 received in 2022-06-19, accepted in 2023-01-05,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAcute Mountain Sickness (AMS) is typically triggered by hypoxia under high altitude conditions. Currently, rule of time among AMS inpatients was not clear. Thus, this study aimed to analyze the time distribution of AMS inpatients in the past ten years and construct a prediction model of AMS hospitalized cases.MethodsWe retrospectively collected medical records of AMS inpatients admitted to the military hospitals from January 2009 to December 2018 and analyzed the time series characteristics. Seasonal Auto-Regressive Integrated Moving Average (SARIMA) was established through training data to finally forecast in the test data set.ResultsA total of 22 663 inpatients were included in this study and recorded monthly, with predominant peak annually, early spring (March) and mid-to-late summer (July to August), respectively. Using the training data from January 2009 to December 2017, the model SARIMA (1, 1, 1) (1, 0, 1) 12 was employed to predict the test data from January 2018 to December 2018. In 2018, the total predicted value after adjustment was 9.24%, less than the actual value.ConclusionAMS inpatients have obvious periodicity and seasonality. The SARIMA model has good fitting ability and high short-term prediction accuracy. It can help explore the characteristics of AMS disease and provide decision-making basis for allocation of relevant medical resources for AMS inpatients.

【 授权许可】

CC BY   
© The Author(s) 2023

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