A Longitudinal Model for a Dynamic Risk Score to Predict Delayed Cerebral Ischemia after Subarachnoid Hemorrhage
A machine learning model for longitudinal DCI risk assessment was developed comprising a static and a dynamic sub-model. The longitudinal performance evaluation highlighted substantial time dependence in model performance, underscoring the need for a longitudinal assessment of prediction models in intensive care settings. Moreover, clinicians need to be aware of these performance variations when performing a risk assessment and weight the different model outputs correspondingly.
Authors
- Carl Muroi
- Jan Willms (ORCID: https://orcid.org/0000-0002-0466-3448)
- J. M. Boss (ORCID: https://orcid.org/0000-0002-8043-0961)
- Stefan Yu Bögli (ORCID: https://orcid.org/0000-0001-5386-0811)
- Corinne Inauen (ORCID: https://orcid.org/0000-0002-7786-2720)
- Emanuela Keller (ORCID: https://orcid.org/0000-0001-7892-8261)
Institutions
- University of Zurich (CH)
- University Hospital of Zurich (CH)
Publication Details
- Journal
- Bioengineering
- Published
- 2024-09-30
- DOI
- https://doi.org/10.3390/bioengineering11100988
- Citations
- 4
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 1.48
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung