Sparse Embedding for Interpretable Hospital Admission Prediction

This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder to embed the original registry data into a much lower dimension, with sparsity as a goal. Then, t-SNE is used to show the embedding of each patient's data in a 2D plot. We then demonstrate the predictive accuracy in different existing machine learning algorithms. Our sparse embedding performs competitively against the original data and traditional embedding vectors with an AUROC of 0.878. In addition, we demonstrate the expressive power of our sparse embedding, i.e. interpretability. Sparse embedding can discover more phenotypes in t-SNE visualization than original data or traditional embedding. The discovered phenotypes can be regarded as different risk groups, through which we can study the driving risk factors for each patient phenotype.