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PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.
Zhang, Siwei; Strayer, Nick; Vessels, Tess; Choi, Karmel; Wang, Geoffrey W; Li, Yajing; Bejan, Cosmin A; Hsi, Ryan S; Bick, Alexander G; Velez Edwards, Digna R; Savona, Michael R; Phillips, Elizabeth J; Pulley, Jill M; Self, Wesley H; Hopkins, Wilkins Consuelo; Roden, Dan M; Smoller, Jordan W; Ruderfer, Douglas M; Xu, Yaomin.
Afiliação
  • Zhang S; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Strayer N; Posit PBC, Boston, MA 02210, United States.
  • Vessels T; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Choi K; Psychiatric & Neuro Developmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States.
  • Wang GW; Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States.
  • Li Y; Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States.
  • Bejan CA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Hsi RS; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Bick AG; Department of Urology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Velez Edwards DR; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Savona MR; Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Phillips EJ; Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Pulley JM; Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Self WH; Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, WA 6150, Australia.
  • Hopkins WC; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Roden DM; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Smoller JW; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Ruderfer DM; Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
  • Xu Y; Psychiatric & Neuro Developmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States.
Article em En | MEDLINE | ID: mdl-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 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.

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Am Med Inform Assoc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Am Med Inform Assoc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos