论文标题
贝叶斯非参数维度降低分类数据，以预测孕妇Covid-19的严重程度
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women
论文作者
论文摘要
冠状病毒疾病（Covid-19）迅速传播到世界各地，虽然孕妇呈现出相同的不良结局率，但在临床研究中她们的人数不足。我们收集了Stony Brook University Hospital的155名测试阳性Covid-19孕妇的临床数据。这些收集的数据中有许多是多元分类类型的，其中可能结果的数量随着数据维度的增加而呈指数增长。我们在无监督的贝叶斯框架中建模了数据，并使用潜在的高斯工艺将它们映射到了较低维度的空间中。进一步使用了较低维空间中的潜在特征，以预测是否由于19号而导致孕妇被送往医院，还是会出现轻度症状。我们将预测准确性与分类数据的虚拟/单热编码进行了比较，发现潜在的高斯过程具有更好的精度。
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.
