  The coronavirus disease (COVID-19) has rapidly spread throughout the world
and while pregnant women present the same adverse outcome rates, they are
underrepresented in clinical research. We collected clinical data of 155
test-positive COVID-19 pregnant women at Stony Brook University Hospital. Many
of these collected data are of multivariate categorical type, where the number
of possible outcomes grows exponentially as the dimension of data increases. We
modeled the data within the unsupervised Bayesian framework and mapped them
into a lower-dimensional space using latent Gaussian processes. The latent
features in the lower dimensional space were further used for predicting if a
pregnant woman would be admitted to a hospital due to COVID-19 or would remain
with mild symptoms. We compared the prediction accuracy with the dummy/one-hot
encoding of categorical data and found that the latent Gaussian process had
better accuracy.
