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Machine learning-based mortality prediction in critically ill patients with hypertension: comparative analysis, fairness, and interpretability
2
Zitationen
4
Autoren
2025
Jahr
Abstract
ML models show strong potential for mortality prediction in critically ill hypertensive patients. Feature selection not only enhances interpretability and reduces computational complexity but may also contribute to improved model fairness. These findings support the integration of interpretable and equitable AI tools in critical care settings to assist with clinical decision-making.
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