The Linked Clinical Data project: Applying Semantic Web technologies for clinical and translational research using electronic medical records

Jyotishman Pathak, Richard C. Kiefer, Christopher G. Chute

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Systematic study of clinical phenotypes is important to better understand the genetic basis of human diseases and more effective gene-based disease management. The Linked Clinical Data (LCD) project at Mayo Clinic aims to develop a semantics-driven framework for high-throughput phenotype extraction, representation, integration, and querying from electronic medical records using emerging Semantic Web technologies, such as Linked Open Data. This poster abstract provides a brief background and overview of the recently initiated LCD project.

Original languageEnglish (US)
Title of host publication4th International Workshop on Semantic Web Applications and Tools for the Life Sciences, SWAT4LS 2011
Pages94-95
Number of pages2
DOIs
StatePublished - Apr 24 2012
Externally publishedYes
Event4th Semantic Web Applications and Tools for the Life Sciences, SWAT4LS 2011 - London, United Kingdom
Duration: Dec 7 2011Dec 9 2011

Publication series

NameACM International Conference Proceeding Series

Other

Other4th Semantic Web Applications and Tools for the Life Sciences, SWAT4LS 2011
CountryUnited Kingdom
CityLondon
Period12/7/1112/9/11

Keywords

  • Diabetes mellitus
  • Genome-wide association studies
  • Genotype-phenotype associations
  • Linked Open Data

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Pathak, J., Kiefer, R. C., & Chute, C. G. (2012). The Linked Clinical Data project: Applying Semantic Web technologies for clinical and translational research using electronic medical records. In 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences, SWAT4LS 2011 (pp. 94-95). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2166896.2166920