Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866909134157250560 |
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| author | Ajirak, Marzieh Heiselman, Cassandra Fuchs, Anna Heiligenstein, Mia Herrera, Kimberly Garretto, Diana Djuric, Petar |
| author_facet | Ajirak, Marzieh Heiselman, Cassandra Fuchs, Anna Heiligenstein, Mia Herrera, Kimberly Garretto, Diana Djuric, Petar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2011_03715 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women Ajirak, Marzieh Heiselman, Cassandra Fuchs, Anna Heiligenstein, Mia Herrera, Kimberly Garretto, Diana Djuric, Petar Applications Machine Learning 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. |
| title | Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2011.03715 |