Creating longitudinal datasets and cleaning existing data identifiers in a cystic fibrosis registry using a novel Bayesian probabilistic approach from astronomy

Peter Donald Hurley, Seb Oliver, Anil Mehta

    Research output: Contribution to journalArticlepeer-review

    122 Downloads (Pure)

    Abstract

    Patient registry data are commonly collected as annual snapshots that need to be amalgamated to understand the longitudinal progress of each patient. However, patient identifiers can either change or may not be available for legal reasons when longitudinal data are collated from patients living in different countries. Here, we apply astronomical statistical matching techniques to link individual patient records that can be used where identifiers are absent or to validate uncertain identifiers. We adopt a Bayesian model framework used for probabilistically linking records in astronomy. We adapt this and validate it across blinded, annually collected data. This is a high-quality (Danish) sub-set of data held in the European Cystic Fibrosis Society Patient Registry (ECFSPR). Our initial experiments achieved a precision of 0.990 at a recall value of 0.987. However, detailed investigation of the discrepancies uncovered typing errors in 27 of the identifiers in the original Danish sub-set. After fixing these errors to create a new gold standard our algorithm correctly linked individual records across years achieving a precision of 0.997 at a recall value of 0.987 without recourse to identifiers. Our Bayesian framework provides the probability of whether a pair of records belong to the same patient. Unlike other record linkage approaches, our algorithm can also use physical models, such as body mass index curves, as prior information for record linkage. We have shown our framework can create longitudinal samples where none existed and validate pre-existing patient identifiers. We have demonstrated that in this specific case this automated approach is better than the existing identifiers.

    Original languageEnglish
    Article numbere0199815
    Pages (from-to)1-15
    Number of pages15
    JournalPLoS ONE
    Volume13
    Issue number7
    DOIs
    Publication statusPublished - 9 Jul 2018

    Keywords

    • Bayes Theorem
    • Cystic Fibrosis/epidemiology
    • Data Accuracy
    • Datasets as Topic/standards
    • Humans
    • Registries

    Fingerprint Dive into the research topics of 'Creating longitudinal datasets and cleaning existing data identifiers in a cystic fibrosis registry using a novel Bayesian probabilistic approach from astronomy'. Together they form a unique fingerprint.

    Cite this