Deep Clustering of Electronic Health Records Tabular Data for Clinical Interpretation.

Machine learning applications are widespread due to straightforward supervised learning of known data labels. Many data samples in real-world scenarios, including medicine, are unlabeled because data annotation can be time-consuming and error-prone. The application and evaluation of unsupervised clustering methods are not trivial and are limited to traditional methods (e.