7 papers · 1 filter
REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation
Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang +1
Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graphs (TKGs) provide a natural foundation by organizing heterogeneous medi…
Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge
Chen Chen, Mohsen Nayebi Kerdabadi, Dongjie Wang +2
Longitudinal prediction from electronic health records (EHRs) is limited by the sparsity and irregularity in patient trajectories, and knowledge augmentation with external knowledg…
Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation
Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Chen Chen +2
In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and procedure codes) is fundamental f…
User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering
Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Dongjie Wang +2
Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existi…
Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Mohsen Nayebi Kerdabadi, William Andrew Byron, Xin Sun +1
Account Takeover (ATO) fraud poses a significant challenge in consumer banking, requiring high recall under strict latency while minimizing friction for legitimate users. Productio…
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation
Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu +2
Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical conditions for prioritizing high…