A Personalized Bayesian Approach for Early Intervention in Gestational Weight Gain Management Toward Pregnancy Care

Abstract

Pre-pregnancy body mass index and weight gain management are associated with pregnancy outcomes in expecting women. Poor gestational weight gain (GWG) management could increase the risk of adverse complications. These risks can be alleviated by lifestyle-based interventions if an undesired GWG trend is detected early on in the pregnancy. Current literature lacks analysis of gestational weight gain data and tracking the pregnancy over time. In this work, we collected longitudinal gestational weight gain data from women during their pregnancy and model their weight measurements to predict the end-of-pregnancy weight gain and classify it in accordance with the medically recommended guidelines. The measurement frequency of the weights is often very variable such that segments of data can be missing and the need to predict early utilising few data points complicates data modelling. We propose a Bayesian approach to forecast weight gain while effectively dealing with the limited data availability for early prediction. We validate on diverse populations from Europe and China. We show that utilising individual’s data only up to mid-way through the pregnancy, our approach produces mean absolute errors of 2.45 kgs and 2.82 kgs in forecasting end-of-pregnancy weight gain on these populations respectively, whereas the best of state-of-the-art yields 8.17 and 6.60 kgs on respective populations. The proposed method can serve as a tool to keep track of an individual’s pregnancy and achieve GWG goals, thus supporting the prevention of excessive or insufficient weight gain during pregnancy.

Keywords

• Gestational weight gain
• missing data
• time series