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Cited by 2Machine Learning in HealthcareDrug-Induced Hepatotoxicity and ProtectionComputational Drug Discovery Methods

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

Huang, Linjun, Shi, Zixin, Tang, Fei, Wang, Haolin·2026·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2025.3590149
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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. 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.

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Authors
Huang, L., Shi, Z., Tang, F., & Wang, H.
Affiliations
Chongqing Medical University (CN)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2026-02
Volume
73
Issue
2
Pages
631-643
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2025.3590149
OpenAlex
W4412490759
Cited by
2
References
41
Citations by year
2026: 2
FWCI
0.81
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (72101040)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
drug-induced liver injury, pediatric patients, dynamic model selection, meta-learning, personalized risk prediction, electronic health records, toxicogenomics, Kolmogorov-Arnold networks, personalized medicine, ensemble selection
MeSH terms
Machine Learning, Algorithms, Child, Humans, Neural Networks, Computer, Chemical and Drug Induced Liver Injury, Electronic Health Records

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