Knowledge-augmented Patient Network Embedding-based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-induced Liver Injury.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Jul 17, 2025
Abstract
OBJECTIVE: To address the challenges of developing machine learning frameworks for Electronic Health Records (EHRs)-based predictive tasks, such as the intricate occurrence mechanism of clinical events, patient diversity, and the inherent limitations of real-world data like data incompleteness and class imbalance, we propose the Knowledge-augmented Patient Network embedding-based Dynamic model Selection (KPNDS) framework, focusing on two key aspects: dynamically selecting the most suitable model for each individual and integrating biomedical knowledge into the framework.
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