Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.
This study highlights the potential of ML-based models for predicting outcomes in CCB poisoning, offering a data-driven framework for early risk stratification. The superior performance of XGBoost and CatBoost suggests their clinical applicability. Future research should focus on external validation in multi-center settings and real-time integration into clinical decision-making systems.
Authors
- Elmira Heidarli
- Amirreza Taherkhani (ORCID: https://orcid.org/0000-0003-0652-9661)
- Seyed Ali Mohtarami (ORCID: https://orcid.org/0000-0003-4426-967X)
- Babak Mostafazadeh (ORCID: https://orcid.org/0000-0003-4872-9610)
- Leena Amine
- Shahin Shadnia (ORCID: https://orcid.org/0000-0002-9401-0781)
- Mojgan Taremi
- Mitra Rahimi (ORCID: https://orcid.org/0000-0002-8785-8207)
- Pooya Eini (ORCID: https://orcid.org/0000-0002-9457-8588)
- Mohammad Meshkini (ORCID: https://orcid.org/0000-0001-6547-140X)
- Mahdi Mehmandoost
- Sayed Masoud Hosseini (ORCID: https://orcid.org/0000-0002-2046-1272)
- Hafedh Thabet (ORCID: https://orcid.org/0009-0003-8751-8012)
- Peyman Erfan Talab Evini
- Nahal Babaeian Amini (ORCID: https://orcid.org/0009-0001-7262-2525)
Institutions
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- PubMed
- Published
- 2025-01-01
- DOI
- https://doi.org/10.22037/aaem.v13i1.2804
- Primary Topic
- Poisoning and overdose treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00