JMIR Publications

ABSTRACT

Background:

Intensive care unit (ICU) electronic health records are sparse, irregularly sampled, and heterogeneous across hospitals, which limits how far patient representation-learning models transfer between centers. Benchmarks that evaluate such models under a common input schema and shared task definitions across multiple ICU databases remain scarce.

Objective:

Our objective is to determine whether Intensive Care Unit (ICU)-specific temporal representation-learning can generalize to support cross-cohort discovery and clinical risk prediction across hospitals. We use a temporal entity–attribute–value (t-EAV) input schema to train models under extreme sparsity, irregular sampling, and cross-center heterogeneity for supervised and unsupervised tasks.

Methods:

We harmonized 350,779 MIMIC-IV, eICU, SICdb, and HiRID stays, and evaluated cohort splits for 16 tasks extending ICU-TSB and YAIB benchmark task definitions. Using t-EAV inputs we evaluated statistical baselines and self-supervised models - recurrent (RNN) and graph neural networks trained from scratch, transformers fine-tuned from pretrained checkpoints and representations extracted zero-shot from general-purpose temporal foundation models.

Results:

No single method ranked highest across the 16 problems; the statistical heuristic (STAT) ranked highest on 6 supervised tasks on the mixed 24-hour cohort with AUROC 0.864 on mortality and 0.789 on sepsis prediction; tSW-GAT ranked highest on readmission (AUROC 0.675). Leiden graph community detection with the bidirectional LSTM reached the highest AMI (0.162) in mixed ICD-stratification. Under the tested zero-shot aggregation interface, generic-domain foundation models mostly trailed, with MOMENT the strongest of them in sepsis on the Fair COMMON test (AUROC 0.517, Appendix Table E3). Statistical heuristic and trained-from-scratch baselines ranked highest on most tasks. The held-out center was challenging for most supervised tasks.

Conclusions:

General-purpose foundation models generally ranked below statistical heuristic and ICU-specific trained-from-scratch models. Several t-EAV baselines achieved higher multi-center performance than single-center cohorts, indicating task-dependent benefits.