From Descriptive to Prescriptive Analytics on Time Series of the Number of Preeclampsia Inpatient Beds

Abstract

One of the most common causes of maternal deaths during pregnancy is preeclampsia. As a result, it is critical to consider the resources required to provide timely care and avoid health complications. One of those resources is hospital beds. Thus, to explore in-depth the inpatient beds occupation to treat preeclampsia at the IESS Los Ceibos Hospital in Guayaquil-Ecuador, Descriptive, Diagnostic, Predictive, and Prescriptive (DDPP) analytics will be applied together to the medical records collected retrospectively. In the descriptive analysis, the effects of the COVID-19 pandemic can be seen during the period March-September 2020. The diagnostic analysis helps us to show a consistent upward trend in the time series. In the predictive analysis, techniques such as Dynamic Bayesian Networks (DBN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) are considered to forecast the future inpatient bed occupation. The model trained with MLP achieved the lowest MAPE, which was 11.58%. It predicts that in September and November of 2024, there will be peaks of 274 and 258 inpatient beds, respectively. Finally, in our prescriptive analysis, we show that closing the hospitalization unit would produce, on average, 130.33 monthly referrals. To our knowledge, we do not find any work that applies all the DDPP analytics together in preeclampsia. Moreover, to our knowledge, this is the first study that makes a joint application of DDPP analytics in univariate time series. Therefore, this work may serve as a basis for future studies in the joint application of all DDPP analytics to time series and to health.

Keywords

• Data analytics
• time series
• hospital management
• dynamic Bayesian networks
• neural networks
• public health data