Patient stratification—identifying clinically meaningful subgroups—enhances personalized medicine by improving diagnostics and treatment strategies. Electronic Health Records (EHRs) capture rich temporal clinical data recorded throughout hospital care. We leverage four public Intensive Care Unit (ICU) EHR datasets to introduce the first benchmark for evaluating patient stratification over temporal patient repre- sentation learning. We compare statistical methods, LSTMs, and GRUs for generating patient representations and assess their effectiveness in clustering patient trajectories. Using ICD and CCS taxonomies, we propose a hierarchical stratifi- cation benchmark to measure alignment with clinically validated disease groupings. Our results demonstrate that temporal representation learning effectively rediscovers clinically meaningful patient cohorts, achieving state-of-the-art clustering. To further enhance interpretability, we evaluate multiple cluster label assignment strategies. The experiments and benchmark are fully reproducible and available at https://github.com/ds4dh/CBMS2025stratification.