Yang Huang

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. ncbi Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon
    Yang Huang
    Stanford Medical Informatics, MSOB X215, 251 Campus Drive, Stanford, CA 94305 5479, USA
    J Am Med Inform Assoc 12:275-85. 2005
  2. ncbi A novel hybrid approach to automated negation detection in clinical radiology reports
    Yang Huang
    Stanford Medical Informatics, Stanford, CA 94305 5479, USA
    J Am Med Inform Assoc 14:304-11. 2007
  3. ncbi A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports
    Yang Huang
    Stanford Medical Informatics, The Office of Information Resources and Technology, Stanford University School of Medicine, California 94305, USA
    J Am Med Inform Assoc 10:580-7. 2003
  4. ncbi Using a statistical natural language Parser augmented with the UMLS specialist lexicon to assign SNOMED CT codes to anatomic sites and pathologic diagnoses in full text pathology reports
    Henry J Lowe
    Center for Clinical Informatics, Stanford University, Stanford, CA, USA
    AMIA Annu Symp Proc 2009:386-90. 2009

Collaborators

Detail Information

Publications4

  1. ncbi Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon
    Yang Huang
    Stanford Medical Informatics, MSOB X215, 251 Campus Drive, Stanford, CA 94305 5479, USA
    J Am Med Inform Assoc 12:275-85. 2005
    ....
  2. ncbi A novel hybrid approach to automated negation detection in clinical radiology reports
    Yang Huang
    Stanford Medical Informatics, Stanford, CA 94305 5479, USA
    J Am Med Inform Assoc 14:304-11. 2007
    ..We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in clinical radiology reports...
  3. ncbi A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports
    Yang Huang
    Stanford Medical Informatics, The Office of Information Resources and Technology, Stanford University School of Medicine, California 94305, USA
    J Am Med Inform Assoc 10:580-7. 2003
    ..This pilot study was performed to evaluate the utility of this indexing approach on a set of clinical radiology reports...
  4. ncbi Using a statistical natural language Parser augmented with the UMLS specialist lexicon to assign SNOMED CT codes to anatomic sites and pathologic diagnoses in full text pathology reports
    Henry J Lowe
    Center for Clinical Informatics, Stanford University, Stanford, CA, USA
    AMIA Annu Symp Proc 2009:386-90. 2009
    ..3% and positive predictive value for diagnostic concepts of 84.4%. The experiment also suggested strategies for improving ChartIndex's performance coding pathology reports...