PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

Area of Science:

• Medical Informatics
• Computational Biology
• Public Health

Background:

• Characterizing multimorbidity patterns is crucial for personalized healthcare.
• Existing tools lack interactive visualization and comprehensive databases for multimorbidity analysis.
• Large-scale electronic health record (EHR) systems contain valuable data for understanding disease associations.

Purpose of the Study:

• To develop an interactive visualization tool and database for characterizing multimorbidity patterns.
• To leverage data from multiple large-scale EHR systems for robust disease association discovery.
• To enhance personalized healthcare strategies through insights into disease multimorbidity.

Main Methods:

• Developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME).
• Integrated summary statistics from phenome-wide analyses of disease multimorbidities across three EHR systems (Vanderbilt, Mass General Brigham, UK Biobank).
• Utilized enhanced associationSubgraphs for dynamic analysis and inference of disease clusters, enabling interactive visualization of multimorbidity networks.

Main Results:

• PheMIME provides interactive and multifaceted visualizations for exploring multimorbidity.
• The tool facilitates the discovery of complex multimorbidity patterns and disease clusters.
• A case study on schizophrenia demonstrated PheMIME's capability in visualizing multimorbidity networks across systems.

Conclusions:

• PheMIME offers an efficient solution for analyzing and interpreting complex EHR data.
• It represents a significant advancement in medical informatics for multimorbidity research.
• PheMIME is the first tool to integrate extensive multimorbidity knowledge from multiple EHRs with interactive online analysis and visualization capabilities.