Vijay Garla

Summary

Affiliation: Yale University
Country: USA

Publications

  1. ncbi Semantic similarity in the biomedical domain: an evaluation across knowledge sources
    Vijay N Garla
    Yale Center for Medical Informatics, Yale University, New Haven, CT 06520 8009, USA
    BMC Bioinformatics 13:261. 2012
  2. ncbi Ontology-guided feature engineering for clinical text classification
    Vijay N Garla
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 501, New Haven, CT 06520 8009, USA
    J Biomed Inform 45:992-8. 2012
  3. ncbi The Yale cTAKES extensions for document classification: architecture and application
    Vijay Garla
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
    J Am Med Inform Assoc 18:614-20. 2011

Collaborators

Detail Information

Publications3

  1. ncbi Semantic similarity in the biomedical domain: an evaluation across knowledge sources
    Vijay N Garla
    Yale Center for Medical Informatics, Yale University, New Haven, CT 06520 8009, USA
    BMC Bioinformatics 13:261. 2012
    ..There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks...
  2. ncbi Ontology-guided feature engineering for clinical text classification
    Vijay N Garla
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 501, New Haven, CT 06520 8009, USA
    J Biomed Inform 45:992-8. 2012
    ..We have released all tools developed as part of this study as open source, available at http://code.google.com/p/ytex...
  3. ncbi The Yale cTAKES extensions for document classification: architecture and application
    Vijay Garla
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
    J Am Med Inform Assoc 18:614-20. 2011
    ..Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges...