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Siwei Zhang is first author of JAMIA paper

Posted by on Tuesday, August 13, 2024 in News.

Congratulations to PhD candidate Siwei Zhang, alumnus Nicholas Strayer (PhD 2020; now at Posit), senior biostatistician Yajing Li, and assistant professor Yaomin Xu on the publication of “PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis” in the Journal of the American Medical Informatics Association on August 10. As stated in the abstract, “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.” Collaborators on the paper include members of Vanderbilt’s Division of Genetic Medicine, Department of Biomedical Informatics, Department of Urology, Department of Obstetrics and Gynecology, Division of Hematology and Oncology, VICTR, Department of Pharmacology, Center for Drug Safety and Immunology, and Department of Psychiatry and Behavioral Sciences, as well as colleagues at Massachusetts General Hospital, North Carolina State University, Murdoch University (Australia), and the Broad Institute. Dr. Xu is corresponding author.

Three-part figure comprising visualization tools for analyzing schizophrenia
Figure 1 from Zhang et al. Reproducible multimorbidity exploration analysis outline for schizophrenia. (A) Interactive Manhattan and scatter plot enable users to select top-co-occurred and consistent disease phenotypes. (B) Interactive table allows users to add or remove disease phenotypes by clicking on the rows in the table and incorporate the updated phenotypes into the final selection by clicking the Update Manhattan/Scatter button, highlighting the user-selected phenotypes. (C) Enhanced associationSubgraphs provides dynamic network analysis to explore disease clusters, highlighting the user-selected phenotypes from part A and B, and their corresponding subgraphs. View this figure in the context of the paper at its JAMIA page.

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