Pneumonia identification using statistical feature selection

J Am Med Inform Assoc. Sep-Oct 2012;19(5):817-23. doi: 10.1136/amiajnl-2011-000752. Epub 2012 Apr 26.

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.

Design: 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.

Conclusion: 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • Data Mining / methods*
  • Diagnosis, Computer-Assisted*
  • Electronic Health Records*
  • Humans
  • Intensive Care Units
  • Natural Language Processing*
  • Pneumonia / diagnosis*
  • Sensitivity and Specificity
  • Unified Medical Language System