An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury
We developed an interpretable ML model capable of accurately predicting myocardial injury in patients with HCSCI. The neural network model showed the best overall performance and, with SHAP interpretation, provided transparent and individualized risk insights, supporting early diagnosis and targeted management in clinical practice.
Authors
- Qinfeng Huang (ORCID: https://orcid.org/0000-0003-3465-0258)
- Jiaqi Li (ORCID: https://orcid.org/0000-0001-6076-4350)
- Hui Chen (ORCID: https://orcid.org/0000-0001-6883-3752)
- Jiaxin Chen (ORCID: https://orcid.org/0000-0003-0321-2060)
- Bingyu Zhang (ORCID: https://orcid.org/0000-0003-2539-4571)
- Ye Liao
- Lijun Lin
- Liqin Wei
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Union Hospital (CN)
Publication Details
- Journal
- Frontiers in Genetics
- Published
- 2025-08-04
- DOI
- https://doi.org/10.3389/fgene.2025.1636065
- Primary Topic
- Spinal Cord Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fujian Medical University