title: Finding falls in ambulatory care clinical documents using statistical text mining.
abstract: OBJECTIVE
  To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter.
  
  
  METHODS
  2241 patients were selected with a fall-related ICD-9-CM E-code or matched injury diagnosis code while being treated as an outpatient at one of four sites within the Veterans Health Administration. All clinical documents within a 48-h window of the recorded E-code or injury diagnosis code for each patient were obtained (n=26 010; 611 distinct document titles) and annotated for falls. Logistic regression, support vector machine, and cost-sensitive support vector machine (SVM-cost) models were trained on a stratified sample of 70% of documents from one location (dataset Atrain) and then applied to the remaining unseen documents (datasets Atest-D).
  
  
  RESULTS
  All three STM models obtained area under the receiver operating characteristic curve (AUC) scores above 0.950 on the four test datasets (Atest-D). The SVM-cost model obtained the highest AUC scores, ranging from 0.953 to 0.978. The SVM-cost model also achieved F-measure values ranging from 0.745 to 0.853, sensitivity from 0.890 to 0.931, and specificity from 0.877 to 0.944.
  
  
  CONCLUSIONS
  The STM models performed well across a large heterogeneous collection of document titles. In addition, the models also generalized across other sites, including a traditionally bilingual site that had distinctly different grammatical patterns.
  
  
  CONCLUSIONS
  The results of this study suggest STM-based models have the potential to improve surveillance of falls. Furthermore, the encouraging evidence shown here that STM is a robust technique for mining clinical documents bodes well for other surveillance-related topics.
authors.affiliation: Consortium for Healthcare Informatics Research (CHIR) and the HSR&D/RR&D Center of Excellence: Maximizing Rehabilitation Outcomes, James A Haley Veterans' Hospital, Tampa, Florida, USA.
authors.forename: James A
authors.initials: JA
authors.lastname: McCart
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Berndt
authors.forename: Jay
authors.initials: J
authors.lastname: Jarman
authors.forename: Dezon K
authors.initials: DK
authors.lastname: Finch
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Luther
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000553:Ambulatory Care; D000555:Ambulatory Care Information Systems; D019540:Area Under Curve; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D060388:Support Vector Machine