Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling
We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance.
Authors
- Henry Xiang (ORCID: https://orcid.org/0000-0001-8112-319X)
- Eric Anthony Sribnick (ORCID: https://orcid.org/0000-0002-1691-8559)
- Rachel Stanley (ORCID: https://orcid.org/0000-0002-4938-3079)
- Biche Osong (ORCID: https://orcid.org/0000-0003-1499-0896)
- Jonathan Ira Groner (ORCID: https://orcid.org/0000-0002-0998-593X)
- Lawrence J. Cook (ORCID: https://orcid.org/0000-0001-9085-0428)
- Bo Lü (ORCID: https://orcid.org/0000-0002-6093-373X)
- Lauren N Schulz
Institutions
- Nationwide Children's Hospital (US)
- University of Utah (US)
- The Ohio State University (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1371/journal.pone.0316462
- Citations
- 2
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.51
Funders
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development