Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908830592401408 |
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| author | Hu, Pengfei Han, Xiaoxue Wang, Fei Ning, Yue |
| author_facet | Hu, Pengfei Han, Xiaoxue Wang, Fei Ning, Yue |
| contents | Domain generalization has become a critical challenge in predictive healthcare, where different patient groups often 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 retrieve domain-invariant (label) information from patient data. On two public datasets, UdonCare shows superiority over eight baselines across four representative clinical prediction tasks with substantial domain gaps, highlighting the potential of medical knowledge for enhancing model generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization Hu, Pengfei Han, Xiaoxue Wang, Fei Ning, Yue Machine Learning Artificial Intelligence Domain generalization has become a critical challenge in predictive healthcare, where different patient groups often 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 retrieve domain-invariant (label) information from patient data. On two public datasets, UdonCare shows superiority over eight baselines across four representative clinical prediction tasks with substantial domain gaps, highlighting the potential of medical knowledge for enhancing model generalization. |
| title | Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06977 |