title: Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database.
abstract: BACKGROUND
  Free-text medication prescriptions contain detailed instruction information that is key when preparing drug data for analysis. The objective of this study was to develop a novel model and automated text-mining method to extract detailed structured medication information from free-text prescriptions and explore their variability (e.g. optional dosages) in primary care research databases.
  
  
  METHODS
  We introduce a prescription model that provides minimum and maximum values for dose number, frequency and interval, allowing modelling variability and flexibility within a drug prescription. We developed a text mining system that relies on rules to extract such structured information from prescription free-text dosage instructions. The system was applied to medication prescriptions from an anonymised primary care electronic record database (Clinical Practice Research Datalink, CPRD).
  
  
  RESULTS
  We have evaluated our approach on a test set of 220 CPRD prescription free-text directions. The system achieved an overall accuracy of 91 % at the prescription level, with 97 % accuracy across the attribute levels. We then further analysed over 56,000 most common free text prescriptions from CPRD records and found that 1 in 4 has inherent variability, i.e. a choice in taking medication specified by different minimum and maximum doses, duration or frequency.
  
  
  CONCLUSIONS
  Our approach provides an accurate, automated way of coding prescription free text information, including information about flexibility and variability within a prescription. The method allows the researcher to decide how best to prepare the prescription data for drug efficacy and safety analyses in any given setting, and test various scenarios and their impact.
authors: School of Computer Science, University of Manchester, Manchester, UK.|The Christie NHS Foundation Trust, Manchester, UK.
authors: George
authors: G
authors: Karystianis
authors: Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK.
authors: Therese
authors: T
authors: Sheppard
authors: Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK.|The Farr Institute of Health Informatics Research, Health eResearch Centre, Manchester, UK.
authors: William G
authors: WG
authors: Dixon
authors: School of Computer Science, University of Manchester, Manchester, UK. g.nenadic@manchester.ac.uk.|The Farr Institute of Health Informatics Research, Health eResearch Centre, Manchester, UK. g.nenadic@manchester.ac.uk.|Manchester Institute of Biotechnology, University of Manchester, Manchester, UK. g.nenadic@manchester.ac.uk.
authors: Goran
authors: G
authors: Nenadic
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D000068240:Data Anonymization; D016208:Databases, Factual; D057286:Electronic Health Records; D055695:Electronic Prescribing; D006801:Humans; D008491:Medical Informatics Applications; D011320:Primary Health Care