Tingyi Wanyan, Mingquan Lin, Eyal Klang, Kartikeya M Menon, Faris F Gulamali, Ariful Azad, Yiye Zhang, Ying Ding, Zhangyang Wang, Fei Wang, Benjamin Glicksberg, Yifan Peng
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Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.
Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).