Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk
The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.
Authors
- Jace D. Johnny (ORCID: https://orcid.org/0000-0001-5798-8230)
- Tracey L. Yap (ORCID: https://orcid.org/0000-0002-3625-5086)
- Jenny Alderden (ORCID: https://orcid.org/0000-0001-6491-1311)
- Yunchuan Zhao (ORCID: https://orcid.org/0000-0001-5737-1177)
- Susan M. Kennerly (ORCID: https://orcid.org/0000-0003-3379-8672)
- Katie Brooks (ORCID: https://orcid.org/0000-0002-0018-1337)
- Andrew Gordon Wilson (ORCID: https://orcid.org/0000-0002-2011-3315)
- Mark van der Laan (ORCID: https://orcid.org/0009-0000-8979-5601)
Institutions
- Boise State University (US)
- PAREXEL International (United Kingdom) (GB)
- Duke University (US)
- United States Institute of Peace (US)
- East Carolina University (US)
- University of Utah (US)
Publication Details
- Journal
- American Journal of Critical Care
- Published
- 2024-09-01
- DOI
- https://doi.org/10.4037/ajcc2024856
- Citations
- 13
- Primary Topic
- Pressure Ulcer Prevention and Management
- Type
- article
- Field-Weighted Citation Impact
- 22.31