ArticleJournal of the American Medical Informatics Association : JAMIA2024
PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multimorbidity patterns and phenotype transitions in patients with clinician-coded long COVID: a multicenter US electronic health record cohort study.BMC infectious diseases · 2026Article
- Multimorbidity quantification from the perspective of precision management: challenges and strategies.Annals of medicine · 2025Review
- Gene and phenome-based analysis of the shared genetic architecture of eye diseases.American journal of human genetics · 2025Article
- Optimization of Health Service Utilization Among Elderly People With Chronic Diseases in Rural Ethnic Minorities in Northwest Yunnan Using Graph Neural Networks.Blockchain in healthcare today · 2025Article
- Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.medRxiv : the preprint server for health sciences · 2024Article
- Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable.Biological psychiatry global open science · 2024Article
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19 authors.
Funding
Abstract
objectivesTo address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies. MATERIALS AND
methodsPheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies.
resultsThe PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME. DISCUSSION: PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records.
conclusionPheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.
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