title: Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.
abstract: "Identifying and labeling" (annotating) sections improves the effectiveness of extracting information stored in the free text of clinical documents. OBSecAn, an automated ontology-based section annotator, was developed to identify and label sections of semi-structured clinical documents from the Department of Veterans Affairs (VA). In the first step, the algorithm reads and parses the document to obtain and store information regarding sections into a structure that supports the hierarchy of sections. The second stage detects and makes correction to errors in the parsed structure. The third stage produces the section annotation output using the final parsed tree. In this study, we present the OBSecAn method and its scale to a million document corpus and evaluate its performance in identifying family history sections. We identify high yield sections for this use case from note titles such as primary care and demonstrate a median rate of 99% in correctly identifying a family history section.
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Le-Thuy T
authors.initials: LT
authors.lastname: Tran
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Marjorie E
authors.initials: ME
authors.lastname: Carter
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Departments of Internal Medicine and Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
mesh_terms: D000465:Algorithms; D066289:Data Curation; D006801:Humans; D009323:Natural Language Processing; D012014:Reference Books, Medical; D014481:United States; D014493:United States Department of Veterans Affairs