1 results match your criteria: "Vanderbilt UniversitySchool of Medicine[Affiliation]"
Evaluation of a method to identify and categorize section headers in clinical documents.
J Am Med Inform Assoc
February 2010
Department of Biomedical Informatics, Vanderbilt UniversitySchool of Medicine, Eskind Biomedical Library, Room 442, 2209 Garland Ave, Nashville TN 37232, USA.
Article Synopsis
- The SecTag algorithm was developed to identify both labeled and implied section headers in clinical notes, specifically "history and physical examination" documents.
- The algorithm employs natural language processing, word recognition, and Bayesian scoring methods to classify note sections.
- Results showed high accuracy with recall and precision rates over 95% for identified sections, indicating its potential utility in clinical decision-making and medical training assessments.