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Development and validation of an interpretable shap-based machine learning model for predicting postoperative complications in laryngeal cancer
1
Zitationen
10
Autoren
2025
Jahr
Abstract
We developed a clinically interpretable ML model that accurately predicts major postoperative complications in patients undergoing laryngeal cancer surgery. This tool provides individualized risk assessments that can guide surgical planning, optimize perioperative strategies, and enhance shared decision-making. Prospective multicenter validation is needed to confirm its utility in routine practice.
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