用于预测孕妇COVID-19重症程度的分类数据贝叶斯非参数降维方法
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women
AI总结：
针对孕妇COVID-19临床研究代表性不足、多分类临床数据维度爆炸的问题，提出基于潜在高斯过程的贝叶斯非参数降维方法，用于预测孕妇COVID-19重症程度，准确率优于传统独热编码方法。
摘要：
冠状病毒病（COVID-19）已在全球迅速传播，尽管孕妇的不良结局发生率与普通人群相近，但她们在临床研究中的代表性不足。我们收集了石溪大学医院155名COVID-19检测阳性孕妇的临床数据。这些收集到的数据多为多变量分类类型，其可能的结果数量会随着数据维度的增加呈指数级增长。我们在无监督贝叶斯框架下对该数据建模，并使用latent Gaussian processes（潜在高斯过程）将其映射到低维空间。低维空间中的潜在特征进一步用于预测孕妇是否会因COVID-19入院，或是仅保持轻症状态。我们将该方法的预测准确率与分类数据的dummy/one-hot encoding（虚拟/独热编码）方法进行了对比，发现潜在高斯过程的准确率更优。
英文摘要：
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.
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