{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mddc-an-r-and-python-package-for-adverse","title":"MDDC: An R and Python Package for Adverse Event Identification in Pharmacovigilance Data","arxiv_id":"2410.01168","date":"2024-10-02","proceeding":null,"authors":["Anran Liu","Raktim Mukhopadhyay","Marianthi Markatou"],"abstract":"The safety of medical products continues to be a significant health concern worldwide. Spontaneous reporting systems (SRS) and pharmacovigilance databases are essential tools for postmarketing surveillance of medical products. Various SRS are employed globally, such as the Food and Drug Administration Adverse Event Reporting System (FAERS), EudraVigilance, and VigiBase. In the pharmacovigilance literature, numerous methods have been proposed to assess product - adverse event pairs for potential signals. In this paper, we introduce an R and Python package that implements a novel pattern discovery method for postmarketing adverse event identification, named Modified Detecting Deviating Cells (MDDC). The package also includes a data generation function that considers adverse events as groups, as well as additional utility functions. We illustrate the usage of the package through the analysis of real datasets derived from the FAERS database.","url_abs":"https://arxiv.org/abs/2410.01168v1","url_pdf":"https://arxiv.org/pdf/2410.01168v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"mddc-an-r-and-python-package-for-adverse","repo_url":"https://github.com/niuniular/MDDC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mddc-an-r-and-python-package-for-adverse","repo_url":"https://github.com/rmj3197/MDDC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}