STOP: Studying Time-Series of Preeclamptic Emergencies

The management of public health in a nation is a crucial matter. The emergency departments are very packed, as in many other countries. To explore in-depth the monthly number of emergency room arrivals of preeclampsia patients during the period 2019-2023 at the "IESS Hospital del Día Sur Valdivia"in Guayaquil, Ecuador, we use descriptive, diagnostic, predictive, and prescriptive (DDPP) analytics together. The descriptive phase considered the benefits of statistics for data characterization. The diagnostic phase is in which the relationships of the trend, seasonality, stationarity, autocorrelation, and anomalies of the time series are reviewed. The predictive phase uses deep learning and statistical models to predict emergency arrivals. The multilayer perceptron model (MLP) achieved the best performance (a mean absolute percentage error of 17.21%), selecting it to forecast the number of preeclamptic emergencies during 2024. Finally, in the prescriptive phase, possible solutions are analyzed using two scenarios presented. The results show each phase of the DDPP analyzes, providing valuable information to improve hospital management. This work can serve as a basis for future studies on the joint application of all DDPP analyses to univariate time series, providing a step-by-step guide on how to analyze such data and introducing a systematic procedure for their analysis for those who may lack statistical expertise.