title: Fast exact string pattern-matching algorithms adapted to the characteristics of the medical language.
abstract: OBJECTIVE
  The authors consider the problem of exact string pattern matching using algorithms that do not require any preprocessing. To choose the most appropriate algorithm, distinctive features of the medical language must be taken into account. The characteristics of medical language are emphasized in this regard, the best algorithm of those reviewed is proposed, and detailed evaluations of time complexity for processing medical texts are provided.
  
  
  METHODS
  The authors first illustrate and discuss the techniques of various string pattern-matching algorithms. Next, the source code and the behavior of representative exact string pattern-matching algorithms are presented in a comprehensive manner to promote their implementation. Detailed explanations of the use of various techniques to improve performance are given.
  
  
  METHODS
  Real-time measures of time complexity with English medical texts are presented. They lead to results distinct from those found in the computer science literature, which are typically computed with normally distributed texts.
  
  
  RESULTS
  The Boyer-Moore-Horspool algorithm achieves the best overall results when used with medical texts. This algorithm usually performs at least twice as fast as the other algorithms tested.
  
  
  CONCLUSIONS
  The time performance of exact string pattern matching can be greatly improved if an efficient algorithm is used. Considering the growing amount of text handled in the electronic patient record, it is worth implementing this efficient algorithm.
authors.affiliation: Puget Sound Health Care System, Seattle, Washington, USA. christian.lovis@dim.hcuge.ch
authors.forename: C
authors.initials: C
authors.lastname: Lovis
authors.forename: R H
authors.initials: RH
authors.lastname: Baud
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D015203:Reproducibility of Results; D009626:Terminology as Topic