Prediction of seizure risk after repetitive mild traumatic brain injury in childhood
A novel machine learning model was developed and validated for personalized prediction of long-term seizure risk following multiple mTBIs. Model performance remained robust with a limited feature set, suggesting the feasibility of real-time incorporation into clinical workflows for individualized prognostication following each repeat mTBI event. In children predicted to be at high risk, early intervention should be considered.
Authors
- Gabriela D. Ruiz Colón (ORCID: https://orcid.org/0000-0001-8380-4511)
- Gerald A. Grant (ORCID: https://orcid.org/0000-0002-2651-4603)
- Laura M. Prolo (ORCID: https://orcid.org/0000-0001-7737-8675)
- Adrian Rodrigues (ORCID: https://orcid.org/0000-0002-9371-1623)
- Bina Kakusa (ORCID: https://orcid.org/0000-0003-0907-2505)
- Jonathon J. Parker (ORCID: https://orcid.org/0000-0002-0531-8400)
- Adela Wu (ORCID: https://orcid.org/0000-0003-4543-9300)
- Solomiia Savchuk (ORCID: https://orcid.org/0000-0003-4167-8603)
- César A. Garcia (ORCID: https://orcid.org/0000-0002-8268-3356)
- Karthik Ravi (ORCID: https://orcid.org/0000-0001-9863-7736)
- Michael C. Jin (ORCID: https://orcid.org/0000-0001-7709-1551)
Institutions
- Lucile Packard Children's Hospital (US)
- Harvard University (US)
- Duke University (US)
- Washington University in St. Louis (US)
- WinnMed (US)
- Massachusetts General Hospital (US)
- Mayo Clinic in Florida (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of Neurosurgery Pediatrics
- Published
- 2025-04-25
- DOI
- https://doi.org/10.3171/2025.1.peds2436
- Primary Topic
- Traumatic Brain Injury and Neurovascular Disturbances
- Type
- article
- Field-Weighted Citation Impact
- 0.00