title: The use of sequential pattern mining to predict next prescribed medications.
abstract: BACKGROUND
  Therapy for certain medical conditions occurs in a stepwise fashion, where one medication is recommended as initial therapy and other medications follow. Sequential pattern mining is a data mining technique used to identify patterns of ordered events.
  
  
  OBJECTIVE
  To determine whether sequential pattern mining is effective for identifying temporal relationships between medications and accurately predicting the next medication likely to be prescribed for a patient.
  
  
  METHODS
  We obtained claims data from Blue Cross Blue Shield of Texas for patients prescribed at least one diabetes medication between 2008 and 2011, and divided these into a training set (90% of patients) and test set (10% of patients). We applied the CSPADE algorithm to mine sequential patterns of diabetes medication prescriptions both at the drug class and generic drug level and ranked them by the support statistic. We then evaluated the accuracy of predictions made for which diabetes medication a patient was likely to be prescribed next.
  
  
  RESULTS
  We identified 161,497 patients who had been prescribed at least one diabetes medication. We were able to mine stepwise patterns of pharmacological therapy that were consistent with guidelines. Within three attempts, we were able to predict the medication prescribed for 90.0% of patients when making predictions by drug class, and for 64.1% when making predictions at the generic drug level. These results were stable under 10-fold cross validation, ranging from 89.1%-90.5% at the drug class level and 63.5-64.9% at the generic drug level. Using 1 or 2 items in the patient's medication history led to more accurate predictions than not using any history, but using the entire history was sometimes worse.
  
  
  CONCLUSIONS
  Sequential pattern mining is an effective technique to identify temporal relationships between medications and can be used to predict next steps in a patient's medication regimen. Accurate predictions can be made without using the patient's entire medication history.
authors.affiliation: Yale School of Medicine, New Haven, CT, United States. Electronic address: Aileen.Wright@yale.edu.
authors.forename: Aileen P
authors.initials: AP
authors.lastname: Wright
authors.affiliation: Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
authors.forename: Adam T
authors.initials: AT
authors.lastname: Wright
authors.affiliation: Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States.
authors.forename: Allison B
authors.initials: AB
authors.lastname: McCoy
authors.affiliation: The University of Texas School of Biomedical Informatics at Houston and the UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX, United States.
authors.forename: Dean F
authors.initials: DF
authors.lastname: Sittig
mesh_terms: D000465:Algorithms; D057225:Data Mining; D020000:Decision Support Systems, Clinical; D003920:Diabetes Mellitus / Q000188:drug therapy; D018450:Disease Progression; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D004358:Drug Therapy / Q000379:methods*; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D015203:Reproducibility of Results; D013453:Sulfonylurea Compounds / Q000627:therapeutic use; D013781:Texas
chemical_list: D013453:Sulfonylurea Compounds
keywords: Clinical decision support; Data mining; Diabetes; Knowledge base; Sequential pattern mining