Development and validation of interpretable machine learning models to predict intensive care unit outcomes in patients on hemodialysis: a multicenter study
ML models provided high predictive accuracy for ICU mortality in hemodialysis patients, facilitating early identification of high-risk individuals and supporting targeted interventions. The online platform promotes clinical translation for intensive care decision-making.
Authors
- Yuanwen Xu (ORCID: https://orcid.org/0009-0005-8835-4050)
- Yunuo Wang
- Jianhua Wu (ORCID: https://orcid.org/0000-0001-6093-599X)
- P. K.-T. Li
- Guoying Huang (ORCID: https://orcid.org/0000-0003-0505-5839)
- Minjie Chen (ORCID: https://orcid.org/0000-0003-2528-1518)
- Zhenghui Li (ORCID: https://orcid.org/0000-0002-3721-4243)
- Yan Q. Xiong (ORCID: https://orcid.org/0000-0002-8732-6948)
- Chintan Pandya (ORCID: https://orcid.org/0000-0003-4133-2514)
Institutions
- Johns Hopkins University (US)
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Huizhou Central People's Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1186/s12911-025-03301-3
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00