Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning Methods
Key predictors of clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health, with children younger than 5 at higher risk. A novel fine Gaussian SVM model outperformed other MLAs, offering high accuracy in predicting outcomes. This tool shows promise for improving clinical decisions while minimizing radiation exposure in children.
Authors
- Asim Mian (ORCID: https://orcid.org/0000-0001-8423-4282)
- Bindu N. Setty (ORCID: https://orcid.org/0000-0002-2749-6822)
- Adrian Gunawan (ORCID: https://orcid.org/0000-0002-9525-4753)
- Maryam Vejdani‐Jahromi (ORCID: https://orcid.org/0000-0002-4503-6264)
- Courtney Takahashi (ORCID: https://orcid.org/0000-0003-3135-527X)
- Artem Kaliaev (ORCID: https://orcid.org/0000-0002-8978-1178)
- Chad W. Farris (ORCID: https://orcid.org/0000-0002-1133-3834)
- Muhammad M. Qureshi (ORCID: https://orcid.org/0000-0002-2056-3308)
Institutions
- Boston Medical Center (US)
- Tufts Medical Center (US)
- Beth Israel Deaconess Medical Center (US)
- John Muir Health (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-08-11
- DOI
- https://doi.org/10.3174/ajnr.a8961
- Citations
- 1
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.25