DiNetxify: a Python package for three-dimensional disease network analysis based on electronic health record data

The rapid expansion of large-scale electronic health record (EHR) data has underscored the necessity for advanced analytical methods, such as disease network analyses, to comprehensively identify and interpret multimorbidity patterns and disease progression pathways. To overcome existing obstacles associated with performing sophisticated disease network analyses on EHR data, we developed DiNetxify , an open-source Python package implementing our recently introduced three-dimensional (3D) disease network analysis method ( https://hzcohort.github.io/DiNetxify/ ). DiNetxify provides a dedicated data class for handling various EHR data, comprehensive modular functions for executing complete 3D disease network analyses, and visualization functions for interactive exploration of results. The package is efficient, user-friendly, and optimized for large-scale EHR datasets. It supports diverse study designs, customizable analysis parameters, and parallel computing for enhanced performance. Through two case studies utilizing the UK Biobank data, one investigating disease networks associated with short leukocyte telomere length and the other exploring disease networks in the middle-aged general population, we demonstrated the capability of DiNetxify to identify meaningful disease clusters and progression patterns consistent with established knowledge while uncovering novel insights. Computationally, the software successfully completed analyses involving cohorts exceeding half a million exposed individuals within 17 hours, using moderate computational resources. We thus anticipate that DiNetxify can significantly reduce technical barriers to facilitate broader adoption of advanced disease network analysis techniques by different researchers, thereby enhancing the exploration of EHR data to improve the understanding of holistic health dynamics.