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Yet, rare conditions in the intensive care unit (ICU), including recognised rare diseases and low-prevalence conditions in the ICU, remain underserved due to data scarcity and intra-condition heterogeneity. To bridge such gaps, we developed KnowRare, a domain adaptation-based deep learning framework for predicting clinical outcomes for rare conditions in the ICU. KnowRare mitigates data scarcity by initially learning condition-agnostic representations from diverse electronic health records through self-supervised pre-training. It addresses intra-condition heterogeneity by selectively adapting knowledge from clinically similar conditions with a developed condition knowledge graph. Evaluated on two ICU datasets across five clinical prediction tasks (90-day mortality, 30-day readmission, ICU mortality, remaining length of stay, and phenotyping), KnowRare consistently outperformed existing state-of-the-art models. Additionally, KnowRare demonstrated superior predictive performance compared to established ICU scoring systems, including APACHE IV and IV-a. Case studies further demonstrated KnowRare\u2019s flexibility in adapting its parameters to accommodate dataset-specific and task-specific characteristics, its generalisation to common conditions under limited data scenarios, and its rationality in selecting source conditions. These findings highlight KnowRare\u2019s potential as a robust and practical solution for supporting clinical decision-making and improving care for rare conditions in the ICU.<\/jats:p>","DOI":"10.1038\/s41746-025-02176-y","type":"journal-article","created":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:32:04Z","timestamp":1767421924000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction"],"prefix":"10.1038","volume":"9","author":[{"given":"Mingcheng","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhiyao","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Tingting","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,3]]},"reference":[{"key":"2176_CR1","volume":"19","author":"CM Wang","year":"2024","unstructured":"Wang, C. 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