Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning
The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.
Authors
- Pedram Sultanian (ORCID: https://orcid.org/0000-0002-6941-6659)
- Peter Lundgren (ORCID: https://orcid.org/0000-0003-0500-4070)
- Vibha Gupta (ORCID: https://orcid.org/0000-0001-6955-9134)
- Emilia Gryska (ORCID: https://orcid.org/0000-0002-7912-2232)
- Araz Rawshani (ORCID: https://orcid.org/0000-0003-2066-3533)
- Fredrik Hessulf (ORCID: https://orcid.org/0000-0003-2730-8710)
- Monér Alchay (ORCID: https://orcid.org/0009-0009-8607-9293)
- Mohammed Mohammed (ORCID: https://orcid.org/0000-0003-4660-8030)
- Tobias Siöland (ORCID: https://orcid.org/0009-0001-7161-7724)
- John Deminger
- Adam Piasecki (ORCID: https://orcid.org/0009-0002-5614-6415)
Institutions
- Sahlgrenska University Hospital (SE)
Publication Details
- Journal
- Resuscitation
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1016/j.resuscitation.2024.110359
- Citations
- 8
- Primary Topic
- Cardiac Arrest and Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 5.53