Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels
This study demonstrated that the XGBoost model, integrating aPL levels and clinical features, offers an effective approach to predicting adverse pregnancy outcomes in APS patients. The model enables clinicians to quickly and accurately identify high-risk pregnancies, providing valuable support for personalized clinical interventions and treatments.
Authors
- Ju Huang (ORCID: https://orcid.org/0000-0003-4280-0690)
- Wanqing Liu (ORCID: https://orcid.org/0000-0001-7727-6900)
- Jun Xiao (ORCID: https://orcid.org/0000-0002-9580-4049)
- Shanling Yan
Institutions
- Deyang Stomatological Hospital (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-08-25
- DOI
- https://doi.org/10.3389/fphys.2025.1617796
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00