Nora: A Vocabulary Discovery Tool for Concept Extraction.

Coverage of terms in domain-specific terminologies and ontologies is often limited in controlled medical vocabularies. Creating and augmenting such terminologies is resource intensive. We developed Nora as an interactive tool to discover terminology from text corpora; the output can then be employed to refine and enhance natural language processing-based concept extraction tasks. Nora provides a visualization of chains of words foraged from word frequency indexes from a text corpus. Domain experts direct and curate chains that contain relevant terms, which are further curated to identify lexical variants. A test of Nora demonstrated an increase of a domain lexicon in homelessness and related psychosocial factors by 38%, yielding an additional 10% extracted concepts.

Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records.

Mining the free text of electronic medical records (EMR) using natural language processing (NLP) is an effective method of extracting information not always captured in administrative data. We sought to determine if concepts related to homelessness, a non-medical condition, were amenable to extraction from the EMR of Veterans Affairs (VA) medical records. As there were no off-the-shelf products, a lexicon of terms related to homelessness was created. A corpus of free text documents from outpatient encounters was reviewed to create the reference standard for NLP training and testing. V3NLP Framework was used to detect instances of lexical terms and was compared to the reference standard. With a positive predictive value of 77% for extracting relevant concepts, this study demonstrates the feasibility of extracting positively asserted concepts related to homelessness from the free text of medical records.