ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts
Journal:
arXiv
Published Date:
Jun 6, 2025
Abstract
Patient stratification identifying clinically meaningful subgroups is
essential for advancing personalized medicine through improved diagnostics and
treatment strategies. Electronic health records (EHRs), particularly those from
intensive care units (ICUs), contain rich temporal clinical data that can be
leveraged for this purpose. In this work, we introduce ICU-TSB (Temporal
Stratification Benchmark), the first comprehensive benchmark for evaluating
patient stratification based on temporal patient representation learning using
three publicly available ICU EHR datasets. A key contribution of our benchmark
is a novel hierarchical evaluation framework utilizing disease taxonomies to
measure the alignment of discovered clusters with clinically validated disease
groupings. In our experiments with ICU-TSB, we compared statistical methods and
several recurrent neural networks, including LSTM and GRU, for their ability to
generate effective patient representations for subsequent clustering of patient
trajectories. Our results demonstrate that temporal representation learning can
rediscover clinically meaningful patient cohorts; nevertheless, it remains a
challenging task, with v-measuring varying from up to 0.46 at the top level of
the taxonomy to up to 0.40 at the lowest level. To further enhance the
practical utility of our findings, we also evaluate multiple strategies for
assigning interpretable labels to the identified clusters. The experiments and
benchmark are fully reproducible and available at
https://github.com/ds4dh/CBMS2025stratification.
