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Journal article
PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis
Journal of the American Medical Informatics Association : JAMIA, Vol.31(11), pp.2440-2446
2024
PMID: 39127052
Abstract
Objectives: To 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 Methods: PheMIME 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 association Subgraphs, 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.
Results: The 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.
Conclusion: PheMIME 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.
Details
- Title
- PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis
- Authors/Creators
- Siwei Zhang - Vanderbilt UniversityNick Strayer - Vanderbilt UniversityTess Vessels - Vanderbilt University Medical CenterKarmel Choi - Massachusetts General HospitalGeoffrey W. Wang - North Carolina State UniversityYajing Li - Vanderbilt University Medical CenterCosmin A. Bejan - Vanderbilt University Medical CenterRyan S. Hsi - Vanderbilt University Medical CenterAlexander G. Bick - Vanderbilt University Medical CenterDigna R. Velez Edwards - Vanderbilt UniversityMichael R. Savona - Vanderbilt University Medical CenterElizabeth J. Phillips - Vanderbilt Univ, Ctr Drug Safety & Immunol, Dept Med, Med Ctr, Nashville, TN 37232 USAJill M. Pulley - Vanderbilt University Medical CenterWesley H. Self - Vanderbilt University Medical CenterWilkins Consuelo Hopkins - Vanderbilt University Medical CenterDan M. Roden - Vanderbilt University Medical CenterJordan W. Smoller - Massachusetts General HospitalDouglas M. Ruderfer - Vanderbilt University Medical CenterYaomin Xu - Vanderbilt University
- Publication Details
- Journal of the American Medical Informatics Association : JAMIA, Vol.31(11), pp.2440-2446
- Publisher
- Oxford University Press on behalf of the American Medical Informatics Association.
- Number of pages
- 8
- Grant note
- Vanderbilt University Department of Biostatistics Development Award UL1TR002243; UL1TR000445; UL1RR024975 / CTSA grants R21DK127075; P50GM115305; R01 MH118233; U01HG004798; R01NS032830; S10RR025141; RC2GM092618; U01HG006378; U19HL065962; R01HD074711 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Identifiers
- 991005692862607891
- Copyright
- © The Author(s) 2024.
- Murdoch Affiliation
- Institute for Immunology and Infectious Diseases
- Language
- English
- Resource Type
- Journal article
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