title: Validation of an association rule mining-based method to infer associations between medications and problems.
abstract: BACKGROUND
  In a prior study, we developed methods for automatically identifying associations between medications and problems using association rule mining on a large clinical data warehouse and validated these methods at a single site which used a self-developed electronic health record.
  
  
  OBJECTIVE
  To demonstrate the generalizability of these methods by validating them at an external site.
  
  
  METHODS
  We received data on medications and problems for 263,597 patients from the University of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice that uses the Allscripts Enterprise commercial electronic health record product. We then conducted association rule mining to identify associated pairs of medications and problems and characterized these associations with five measures of interestingness: support, confidence, chi-square, interest and conviction and compared the top-ranked pairs to a gold standard.
  
  
  RESULTS
  25,088 medication-problem pairs were identified that exceeded our confidence and support thresholds. An analysis of the top 500 pairs according to each measure of interestingness showed a high degree of accuracy for highly-ranked pairs.
  
  
  CONCLUSIONS
  The same technique was successfully employed at the University of Texas and accuracy was comparable to our previous results. Top associations included many medications that are highly specific for a particular problem as well as a large number of common, accurate medication-problem pairs that reflect practice patterns.
authors: A
authors: A
authors: Wright
authors: A
authors: A
authors: McCoy
authors: S
authors: S
authors: Henkin
authors: M
authors: M
authors: Flaherty
authors: D
authors: D
authors: Sittig
mesh_terms: D057225:Data Mining / Q000379:methods*; D062313:Databases, Pharmaceutical; D057286:Electronic Health Records; D005240:Feasibility Studies; D006801:Humans; D004364:Pharmaceutical Preparations; D015203:Reproducibility of Results; D013781:Texas; D014495:Universities
chemical_list: D004364:Pharmaceutical Preparations
keywords: Problem list; automated inference; clinical decision support; data mining; methodology
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