title: Validating a strategy for psychosocial phenotyping using a large corpus of clinical text.
abstract: OBJECTIVE
  To develop algorithms to improve efficiency of patient phenotyping using natural language processing (NLP) on text data. Of a large number of note titles available in our database, we sought to determine those with highest yield and precision for psychosocial concepts.
  
  
  METHODS
  From a database of over 1 billion documents from US Department of Veterans Affairs medical facilities, a random sample of 1500 documents from each of 218 enterprise note titles were chosen. Psychosocial concepts were extracted using a UIMA-AS-based NLP pipeline (v3NLP), using a lexicon of relevant concepts with negation and template format annotators. Human reviewers evaluated a subset of documents for false positives and sensitivity. High-yield documents were identified by hit rate and precision. Reasons for false positivity were characterized.
  
  
  RESULTS
  A total of 58 707 psychosocial concepts were identified from 316 355 documents for an overall hit rate of 0.2 concepts per document (median 0.1, range 1.6-0). Of 6031 concepts reviewed from a high-yield set of note titles, the overall precision for all concept categories was 80%, with variability among note titles and concept categories. Reasons for false positivity included templating, negation, context, and alternate meaning of words. The sensitivity of the NLP system was noted to be 49% (95% CI 43% to 55%).
  
  
  CONCLUSIONS
  Phenotyping using NLP need not involve the entire document corpus. Our methods offer a generalizable strategy for scaling NLP pipelines to large free text corpora with complex linguistic annotations in attempts to identify patients of a certain phenotype.
authors.affiliation: IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.forename: Marjorie
authors.initials: M
authors.lastname: Carter
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: Miland
authors.initials: M
authors.lastname: Palmer
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010641:Phenotype; D011584:Psychology; D014481:United States; D014493:United States Department of Veterans Affairs
keywords: clinical informatics; high through-put; natural language processing; patient phenotype; psychosocial concepts