title: Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals.
abstract: OBJECTIVE
  The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous studies.
  
  
  METHODS
  In this work, a support vector machine (SVM)-based natural language processing (NLP) algorithm is trained to classify epilepsy progress notes as belonging to a patient with a specific type of epilepsy from a particular hospital. The same SVM is then used to classify notes from another hospital. Our null hypothesis is that an NLP algorithm cannot be trained using epilepsy-specific notes from one hospital and subsequently used to classify notes from another hospital better than a random baseline classifier. The hypothesis is tested using epilepsy progress notes from the three hospitals.
  
  
  RESULTS
  We are able to reject the null hypothesis at the 95% level. It is also found that classification was improved by including notes from a second hospital in the SVM training sample.
  
  
  CONCLUSIONS
  With a reasonably uniform epilepsy vocabulary and an NLP-based algorithm able to use this uniformity to classify epilepsy progress notes across different hospitals, we can pursue automated comparisons of patient conditions, treatments, and diagnoses across different healthcare settings.
authors.affiliation: Cincinnati Children's Hospital Hospital Medical Center, Division of Bioinformatics, Cincinnati, Ohio, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Connolly
authors.affiliation: Cincinnati Children's Hospital Hospital Medical Center, Division of Bioinformatics, Cincinnati, Ohio, USA.
authors.forename: Pawel
authors.initials: P
authors.lastname: Matykiewicz
authors.affiliation: University of Colorado School of Medicine, Biomedical Text Mining Group, Computational Bioscience Program, Aurora, Colorado, USA.
authors.forename: K
authors.initials: K
authors.lastname: Bretonnel Cohen
authors.affiliation: Cincinnati Children's Hospital Medical Center, Division of Pediatric Neurology, Cincinnati, Ohio, USA.
authors.forename: Shannon M
authors.initials: SM
authors.lastname: Standridge
authors.affiliation: Cincinnati Children's Hospital Medical Center, Division of Pediatric Neurology, Cincinnati, Ohio, USA.
authors.forename: Tracy A
authors.initials: TA
authors.lastname: Glauser
authors.affiliation: The Children's Hospital of Philadelphia, Division of Child Neurology, Philadelphia, Pennsylvania, USA.
authors.forename: Dennis J
authors.initials: DJ
authors.lastname: Dlugos
authors.affiliation: University of Colorado/Children's Hospital Colorado, Division of Pediatric Neurology, Aurora, Colorado, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Koh
authors.affiliation: University of Colorado School of Medicine, Department of Pediatrics, Aurora, Colorado, USA Children's Hospital Colorado, Research Informatics, Aurora, Colorado, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Tham
authors.affiliation: Cincinnati Children's Hospital Hospital Medical Center, Division of Bioinformatics, Cincinnati, Ohio, USA.
authors.forename: John
authors.initials: J
authors.lastname: Pestian
mesh_terms: D057286:Electronic Health Records; D004827:Epilepsy; D006776:Hospitals, Pediatric; D006801:Humans; D008037:Linguistics; D009323:Natural Language Processing; D060388:Support Vector Machine; D009626:Terminology as Topic
keywords: Epilepsy; Linguistics; Multicenter; Support vector machines; Text classification