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DiNetxify-a python package for three‑dimensional disease network analysis based on electronic health record data
Can Hou1,2,3, Haowen Liu2,4, Viktor H Ahlqvist3,5
1Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
European Journal of Epidemiology
|January 24, 2026
Summary
DiNetxify, a new Python package, simplifies complex disease network analysis using electronic health records (EHRs). It helps researchers identify multimorbidity patterns and disease progression from large datasets efficiently.
Area of Science:
- Computational biology and bioinformatics
- Health informatics and data science
Background:
- Large-scale electronic health record (EHR) data necessitates advanced analytical methods for understanding multimorbidity and disease progression.
- Existing methods for disease network analysis on EHR data face significant technical obstacles.
Purpose of the Study:
- To introduce DiNetxify, an open-source Python package for performing three-dimensional (3D) disease network analyses on EHR data.
- To overcome technical barriers and facilitate the adoption of advanced disease network analysis techniques by researchers.
Main Methods:
- Developed DiNetxify, a Python package with a dedicated data class for EHR data, modular functions for 3D disease network analysis, and interactive visualization tools.
- Implemented parallel computing and optimization for large-scale datasets, supporting diverse study designs and customizable parameters.
- Conducted a case study using UK Biobank data to analyze disease networks associated with short leukocyte telomere length.
Main Results:
- DiNetxify successfully identified meaningful disease clusters and progression patterns from large-scale EHR data, aligning with existing knowledge and revealing novel insights.
- The software efficiently processed large cohorts (over 500,000 individuals) within 17 hours using moderate computational resources.
- Demonstrated the package's capability to handle complex analyses and provide interactive exploration of results.
Conclusions:
- DiNetxify significantly reduces technical barriers for researchers, promoting broader use of advanced disease network analysis on EHR data.
- The package enhances the exploration of holistic health dynamics and disease progression pathways from comprehensive health records.
- Anticipated to improve understanding of complex health conditions and facilitate data-driven clinical insights.
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