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Familial, Associational, & Incidental Relationships (FAIR)
Community Engagement and Research Symposia
  • Thomas M. English, University of Massachusetts Medical School
  • Michael J. Davis, University of Massachusetts Medical School
  • Rebecca L. Kinney, University of Massachusetts Medical School
  • Ariana Kamberi, University of Massachusetts Medical School
  • Wayne Chan, University of Massachusetts Medical School
  • Rajani S. Sadasivam, University of Massachusetts Medical School
  • Thomas K. Houston, University of Massachusetts Medical School
Start Date
7-11-2014 8:00 AM
Description

Identifying familial linkages in a phenotypic data warehouse can be valuable in cohort identification, and beginning to understand interactions of diseases among families. The goal of the Familial, Associational, & Incidental Relationships (FAIR) system is to identify an index set patients’ relationships through elements in a data warehouse. Using a test set of 500 children, we measured the sensitivity and specificity of available linkage algorithm (e.g.: insurance id and phone numbers) and validated this tool/algorithm through a manual chart audit. Sensitivity varied from 16% to 87%, and specificity from 70% to 100% using various combinations of identifiers. Using the “i2b2” warehouse infrastructure, we have now developed a web app that facilitates FAIR for any index population.

Keywords
  • phenotypes,
  • familial relationships,
  • clinical data warehouse
Comments

Poster presented at the 2014 UMass Center for Clinical and Translational Science Community Engagement and Research Symposium, held on November 7, 2014 at the University of Massachusetts Medical School, Worcester, Mass.

Creative Commons License
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Citation Information
Thomas M. English, Michael J. Davis, Rebecca L. Kinney, Ariana Kamberi, et al.. "Familial, Associational, & Incidental Relationships (FAIR)" (2014)
Available at: http://works.bepress.com/rajani_sadasivam/32/