Knowledge-Augmented Patient Network Embedding-Based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-Induced Liver Injury

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. METHODS: KPNDS utilizes graph machine learning algorithms to generate patient embeddings from a knowledge-augmented network which integrates data from a diverse range of data sources including EHRs, drug-related information, toxicogenomics data and other relevant information to enrich the understanding of patients. A meta-learning based framework is adopted to dynamically select the optimal classifiers based on the latent patient representations to perform individualized risk prediction. Multi-Layer Perceptron, Transformer and Kolmogorov-Arnold Networks are used as meta-classifiers to enhance the selection of the optimal classifiers for each patient. RESULTS: The KPNDS framework was validated for the early prediction of drug-induced liver injury (DILI) in pediatric patients. Experimental results show that it outperforms common baseline models and dynamic ensemble selection methods. CONCLUSION: The KPNDS framework effectively integrates domain knowledge, graph-based machine learning and dynamic model selection strategies, thereby enhancing predictive performance. SIGNIFICANCE: The KPNDS framework seamlessly integrates knowledge-augmented networks with dynamic model selection techniques, which has the potential to enable more accurate risk assessment and personalized medicine in complex scenarios, highlighting a novel approach to integrating external knowledge with data-driven models.