title: Pneumonia identification using statistical feature selection.
abstract: OBJECTIVE
  This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.
  
  
  METHODS
  A binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification.
  
  
  RESULTS
  Besides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively.
  
  
  CONCLUSIONS
  Using a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.
authors.affiliation: Department of Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, Washington 98195-7240, USA. bejan@u.washington.edu
authors.forename: Cosmin Adrian
authors.initials: CA
authors.lastname: Bejan
authors.forename: Fei
authors.initials: F
authors.lastname: Xia
authors.forename: Lucy
authors.initials: L
authors.lastname: Vanderwende
authors.forename: Mark M
authors.initials: MM
authors.lastname: Wurfel
authors.forename: Meliha
authors.initials: M
authors.lastname: Yetisgen-Yildiz
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted; D057286:Electronic Health Records; D006801:Humans; D007362:Intensive Care Units; D009323:Natural Language Processing; D011014:Pneumonia / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D017432:Unified Medical Language System