Early prognostic factors of patients with acquired pneumonia under the analysis of autoregressive integrated moving average model-based pathogenic infectious influenza virus

Abstract

In view of the high mortality rate and complications of acquired pneumonia (AP), the improved autoregressive integrated moving average (ARIMA) model was applied to predict the early prognostic factors of patients with AP. First, a multi-factor mixed forecast ARIMA-classification and regression trees (CART) classification tree model was established in this study, and the ARIMA-CART model was used for time series fitting analysis to observe the incidence trend of AP. Finally, a predictive model for the risk and prognosis of elderly patients with AP was constructed. The experimental results proved that the serum creatine kinase index and lactate dehydrogenase index of patients with AP had a resistance effect on their prognosis. Therefore, the serum creatine kinase index and lactate dehydrogenase index of patients with AP should be dealt with in actual medical scenarios. The indexes can be reasonably detected and can effectively improve the prognosis of patients. This had a certain reference for the promotion of ARIMA model in the research of early prognostic factors in patients with AP.

Keywords

• Differential autoregressive moving average model
• Prognostic factors
• Acquired pneumonia
• Time series