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A Minimal Information Model for Potential Drug-Drug Interactions.
Hochheiser, Harry; Jing, Xia; Garcia, Elizabeth A; Ayvaz, Serkan; Sahay, Ratnesh; Dumontier, Michel; Banda, Juan M; Beyan, Oya; Brochhausen, Mathias; Draper, Evan; Habiel, Sam; Hassanzadeh, Oktie; Herrero-Zazo, Maria; Hocum, Brian; Horn, John; LeBaron, Brian; Malone, Daniel C; Nytrø, Øystein; Reese, Thomas; Romagnoli, Katrina; Schneider, Jodi; Zhang, Louisa Yu; Boyce, Richard D.
Afiliación
  • Hochheiser H; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Jing X; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.
  • Garcia EA; Department of Public Health Sciences, Clemson University, Clemson, SC, United States.
  • Ayvaz S; Pharmacy Consulting International (PCI), New York City, NY, United States.
  • Sahay R; Department of Software Engineering, Bahçesehir University, Istanbul, Turkey.
  • Dumontier M; Clinical Data Science, AstraZeneca, Cambridge, United Kingdom.
  • Banda JM; Institute of Data Science, Maastricht University, Maastricht, Netherlands.
  • Beyan O; Department of Computer Science, Georgia State University, Atlanta, GA, United States.
  • Brochhausen M; Fraunhofer Institute for Applied Information Technology, RWTH Aachen University, Aachen, Germany.
  • Draper E; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States.
  • Habiel S; Mayo Clinic, Rochester, MN, United States.
  • Hassanzadeh O; Open Source Electronic Health Record Alliance, Washington, DC, United States.
  • Herrero-Zazo M; IBM Research, Yorktown Heights, NY, United States.
  • Hocum B; The European Bioinformatics Institute, Birney Research Group, London, United Kingdom.
  • Horn J; Genelex Corporation, Seattle, WA, United States.
  • LeBaron B; School of Pharmacy, University of Washington, Seattle, WA, United States.
  • Malone DC; Southeast Louisiana Veterans Health Care System, New Orleans, LA, United States.
  • Nytrø Ø; Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States.
  • Reese T; Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
  • Romagnoli K; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
  • Schneider J; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Zhang LY; School of Information Science, University of Illinois, Champaign, IL, United States.
  • Boyce RD; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
Front Pharmacol ; 11: 608068, 2020.
Article en En | MEDLINE | ID: mdl-33762928
ABSTRACT
Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https//w3id.org/hclscg/pddi.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Front Pharmacol Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Front Pharmacol Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos