A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study
We present a machine learning model that can predict the risk of IVIG non-responsiveness in patients with KD in China. This model may help doctors develop personalized treatment strategies, thus improving the prognosis of KD.
Authors
- Zihan Zhou (ORCID: https://orcid.org/0000-0002-1697-2168)
- Lin Zhao (ORCID: https://orcid.org/0000-0002-1851-884X)
- Xu Ma (ORCID: https://orcid.org/0000-0003-0644-7498)
- Ce Wang (ORCID: https://orcid.org/0000-0003-3917-529X)
- Yujian Wu (ORCID: https://orcid.org/0000-0003-1044-6797)
- Xuemei Li (ORCID: https://orcid.org/0000-0002-3384-1987)
- Lu Sun (ORCID: https://orcid.org/0000-0002-6592-1156)
- Dong Li (ORCID: https://orcid.org/0000-0002-5414-8786)
- Jingyi Fan
- Zhouping Wang (ORCID: https://orcid.org/0000-0003-3449-7687)
- Rui Xu (ORCID: https://orcid.org/0009-0000-0827-5023)
Institutions
- Guangzhou Women and Children Medical Center (CN)
- China Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3389/fimmu.2025.1629600
- Primary Topic
- Kawasaki Disease and Coronary Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Medical University