Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.
Journal:
AMIA ... Annual Symposium proceedings. AMIA Symposium
PMID:
26958260
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.