Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation
This research underscores the potential of machine learning models to enhance the accuracy of outcome predictions in SCI rehabilitation. The findings support the integration of these advanced predictive tools in clinical settings to better guide decision making for patients and families, tailor rehabilitation plans, allocate resources efficiently, and ultimately improve patient outcomes.
Authors
- Irene Say
- Emily R. Rosario (ORCID: https://orcid.org/0000-0002-1540-197X)
- Yan Zhou (ORCID: https://orcid.org/0000-0002-7907-1148)
- Ning Zhang (ORCID: https://orcid.org/0000-0002-3763-8361)
- Xinran Liu (ORCID: https://orcid.org/0000-0001-8559-591X)
- Peter Wu (ORCID: https://orcid.org/0000-0003-2700-9403)
- Daniel C. Lu
- Mohammad Rasoolinejad
Institutions
- University of California, Los Angeles (US)
- Casa Colina Centers for Rehabilitation (US)
- University of Colorado Anschutz Medical Campus (US)
Publication Details
- Journal
- Frontiers in Rehabilitation Sciences
- Published
- 2025-08-25
- DOI
- https://doi.org/10.3389/fresc.2025.1594753
- Citations
- 2
- Primary Topic
- Spinal Cord Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 2.94