Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database

BMC Medical Informatics and Decision Making

Research Article

Therese Sheppard¹  William G. Dixon²  George Karystianis³  Goran Nenadic⁴ 

[1] Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK;Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK;The Farr Institute of Health Informatics Research, Health eResearch Centre, Manchester, UK;School of Computer Science, University of Manchester, Manchester, UK;The Christie NHS Foundation Trust, Manchester, UK;School of Computer Science, University of Manchester, Manchester, UK;The Farr Institute of Health Informatics Research, Health eResearch Centre, Manchester, UK;Manchester Institute of Biotechnology, University of Manchester, Manchester, UK;

关键词: Text mining;    Natural language processing;    Dose information;    Prescriptions;    CPRD;   

DOI  :  10.1186/s12911-016-0255-x

 received in 2015-08-14, accepted in 2016-01-28,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundFree-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.MethodsWe 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).ResultsWe 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.ConclusionsOur 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.

【 授权许可】

CC BY   
© Karystianis et al. 2016