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Machine Learning Approaches for Predicting Intraoperative Blood Transfusion in Partial Hip Arthroplasty
2
Zitationen
1
Autoren
2025
Jahr
Abstract
Machine learning models-particularly Random Forest, Decision Tree, and SVM-demonstrated high performance in predicting intraoperative transfusion needs during PHA. The incorporation of explainable AI techniques such as SHAP enhanced the clinical interpretability of model outputs, supporting personalized patient management. These findings provide a strong foundation for integrating such models into clinical decision support systems, though external validation through multicenter and prospective studies is warranted.
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