mlsanityData to 14 Oct 2026

Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization

arXiv 2506.06977 ↗First posted 8 Jun 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

Domain generalization has become a critical challenge in predictive healthcare, where different patient groups exhibit shifting data distributions that degrade model performance. Still, regular domain generalization approaches often struggle in clinical settings due to (1) the absence of domain labels and (2) the lack of clinical insight integration. To address these challenges in healthcare, we aim to explore how medical ontologies can be used to discover dynamic yet hierarchy-grounded patient domains, a partitioning strategy that remains under-explored in prior work. Hence, we introduce UdonCare, a hierarchy-pruning method that iteratively divides patients into latent domains and retrieves domain-invariant (label) information from patient data. On public datasets (MIMIC-III, MIMIC-IV, and eICU), UdonCare shows superiority over eight generalization baselines across four representative clinical prediction tasks with substantial domain gaps, highlighting the potential of medical knowledge for enhancing model generalization.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 76 references that are in the map. First posted on arXiv 8 Jun 2025.

Builds on

34 of 76 references.

References · 34

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22 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guaranteescited by 3
  2. 2026Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inferencecited by 1
  3. 2025HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Predictioncited by 0