108 citations · 147 across the 7 of their papers we have counts for
6 papers · 1 filter
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update
Bizhan Alipour Pijan, Serdar Bozdag
Most of the dynamic graph representation learning methods involve dividing a dynamic graph into discrete snapshots to capture the evolving behavior of nodes over time. Existing met…
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings
Mohammad Al Olaimat, Shaika Chowdhury, Serdar Bozdag
Electronic Health Records (EHRs) contain rich, longitudinal patient information across structured (e.g., labs, vitals, and imaging) and unstructured (e.g., clinical notes) modaliti…
Longitudinal Ensemble Integration for sequential classification with multimodal data
Aviad Susman, Rupak Krishnamurthy, Yan Chak Li +5
Effectively modeling multimodal longitudinal data is a pressing need in various application areas, especially biomedicine. Despite this, few approaches exist in the literature for…
IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration
Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath +2
Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine resear…
TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records
Mohammad Al Olaimat, Serdar Bozdag
Motivation: Electronic Health Records (EHR) represent a comprehensive resource of a patient's medical history. EHR are essential for utilizing advanced technologies such as deep le…
GRAF: Graph Attention-aware Fusion Networks
Ziynet Nesibe Kesimoglu, Serdar Bozdag
A large number of real-world networks include multiple types of nodes and edges. Graph Neural Network (GNN) emerged as a deep learning framework to generate node and graph embeddin…