title: Medication information extraction with linguistic pattern matching and semantic rules.
abstract: OBJECTIVE
  This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason.
  
  
  METHODS
  The system implements a rule-based methodology, which exploits typical morphological, lexical, syntactic and semantic features of the targeted information. These features were acquired from the training dataset and public resources such as the UMLS and relevant web pages. Information extracted by pattern matching was combined together using context-sensitive heuristic rules.
  
  
  METHODS
  The system was applied to a set of 547 previously unseen discharge summaries, and the extracted information was evaluated against a manually prepared gold standard consisting of 251 documents. The overall ranking of the participating teams was obtained using the micro-averaged F-measure as the primary evaluation metric.
  
  
  RESULTS
  The implemented method achieved the micro-averaged F-measure of 81% (with 86% precision and 77% recall), which ranked this system third in the challenge. The significance tests revealed the system's performance to be not significantly different from that of the second ranked system. Relative to other systems, this system achieved the best F-measure for the extraction of duration (53%) and reason (46%).
  
  
  CONCLUSIONS
  Based on the F-measure, the performance achieved (81%) was in line with the initial agreement between human annotators (82%), indicating that such a system may greatly facilitate the process of extracting relevant information from medical records by providing a solid basis for a manual review process.
authors.affiliation: Cardiff School of Computer Science & Informatics, Cardiff University, Cardiff, UK. i.spasic@cs.cardiff.ac.uk
authors.forename: Irena
authors.initials: I
authors.lastname: Spasic
authors.forename: Farzaneh
authors.initials: F
authors.lastname: Sarafraz
authors.forename: John A
authors.initials: JA
authors.lastname: Keane
authors.forename: Goran
authors.initials: G
authors.lastname: Nenadic
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations; D012660:Semantics
chemical_list: D004364:Pharmaceutical Preparations
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