Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

ArXi:2604.07085v1 Announce Type: new In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making. However, clustering in healthcare informatics is still based on traditional methods, especially K-means, and has achieved limited success when applied to embedding representations learned by autoencoders as hybrid methods.