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Updated: Feb 14, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

DyGraphTrans: A temporal graph representation learning framework for modeling disese progression from Electronic

Most Tahmina Rahman^1,2,3, Mohammad Al Olaimat⁴, Serdar Bozdag^1,2,3,5

¹Department of Computer Science & Engineering, University of North Texas, Denton, TX 76203, USA.

Biorxiv : the Preprint Server for Biology

|February 13, 2026

Summary

This summary is machine-generated.

DyGraphTrans offers a novel framework for early disease prediction using Electronic Health Records (EHRs). This dynamic graph approach efficiently handles patient data, improving accuracy and interpretability for clinical insights.

Keywords:

Alzheimer’s Disease Electronic Health Records Graph Neural Networks Graph Representation Learning

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Area of Science:

Computational biology
Medical informatics
Machine learning

Background:

Electronic Health Records (EHRs) offer rich longitudinal patient data for disease prediction.
Existing computational methods for EHR analysis often suffer from high memory usage, computational cost, and lack of interpretability.
Efficiently processing large-scale EHR data while maintaining accuracy and interpretability is a significant challenge.

Purpose of the Study:

To introduce DyGraphTrans, a dynamic graph representation learning framework for patient EHR data.
To address the limitations of existing methods in terms of memory consumption, computational cost, and interpretability.
To enable accurate and interpretable early disease prediction from EHRs.

Main Methods:

Representing patient EHR data as a sequence of temporal graphs.
Utilizing nodes for patients, node features for temporal clinical attributes, and edges for patient similarity.
Employing a sliding-window mechanism to reduce memory consumption while preserving temporal context.
Jointly capturing patient similarity and temporal evolution in a memory-efficient and interpretable manner.

Main Results:

DyGraphTrans demonstrated strong predictive performance on Alzheimer's Disease Neuroimaging Initiative (ADNI), National Alzheimer's Coordinating Center (NACC), and Medical Information Mart for Intensive Care (MIMIC-IV) datasets.
The model achieved accurate early mortality prediction and disease progression prediction.
Interpretability analysis showed alignment with known clinical risk factors.

Conclusions:

DyGraphTrans provides an efficient and interpretable solution for leveraging EHR data for disease prediction.
The framework successfully models local temporal dependencies and long-range global trends in patient data.
DyGraphTrans offers a promising approach for advancing computational methods in clinical informatics and precision medicine.