| Yazarlar (4) |
Onur Akgün
Türkiye |
|
Murat Akdoğan
Türkiye |
Arş. Gör. Ahmet ARDAHANLI
Kafkas Üniversitesi, Türkiye |
|
İsa Ardahanlı
Bilecik Şeyh Edebali Üniversitesi, Türkiye |
| Özet |
| To the Editor, We commend Chaudhary et al. 1 for their insightful study,“Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant,” published in The American Journal of Cardiology. Their work rigorously compares machine learning algorithms—random forest and XGBoost—to conventional bleeding risk scores (HASBLED, ORBIT, ATRIA) in predicting major bleeding events among atrial fibrillation (AF) patients receiving direct oral anticoagulants (DOACs). The authors report superior discriminative performance of machine learning models (AUC: 0.76) over traditional scores (AUC: 0.57 for HAS-BLED), underscoring machine learning’s potential to refine personalized risk stratification. Notably, their SHAP (SHapley Additive exPlanations) analysis identified novel predictors, such as body mass index, lipid profiles, and insurance type, which may elucidate previously … |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | The American Journal of Cardiology |
| Dergi ISSN | 0002-9149 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q3 |
| Makale Dili | Türkçe |
| Basım Tarihi | 07-2025 |
| Cilt No | 246 |
| Doi Numarası | 10.1016/j.amjcard.2025.03.015 |
| Makale Linki | https://doi.org/10.1016/j.amjcard.2025.03.015 |
| Atıf Sayıları | |
| Google Scholar | 2 |
