title: A cascade of classifiers for extracting medication information from discharge summaries.
abstract: BACKGROUND
  Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.
  
  
  METHODS
  We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.
  
  
  RESULTS
  The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.
  
  
  CONCLUSIONS
  This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.
authors: University of Washington, PO Box 543450, Seattle, WA 98195, USA. captnpi@uw.edu.
authors: Scott Russell
authors: SR
authors: Halgrim
authors: Fei
authors: F
authors: Xia
authors: Imre
authors: I
authors: Solti
authors: Eithon
authors: E
authors: Cadag
authors: Ozlem
authors: O
authors: Uzuner