BackgroundMedical research networks rely on record linkage and pseudonymization to determine which records from different sources relate to the same patient. To establish informational separation of powers, the required identifying data are redirected to a trusted third party that has, in turn, no access to medical data. This pseudonymization service receives identifying data, compares them with a list of already reported patient records and replies with a (new or existing) pseudonym. We found existing solutions to be technically outdated, complex to implement or not suitable for internet-based research infrastructures. In this article, we propose a new RESTful pseudonymization interface tailored for use in web applications accessed by modern web browsers.MethodsThe interface is modelled as a resource-oriented architecture, which is based on the representational state transfer (REST) architectural style. We translated typical use-cases into resources to be manipulated with well-known HTTP verbs. Patients can be re-identified in real-time by authorized users’ web browsers using temporary identifiers. We encourage the use of PID strings for pseudonyms and the EpiLink algorithm for record linkage. As a proof of concept, we developed a Java Servlet as reference implementation.ResultsThe following resources have been identified: Sessions allow data associated with a client to be stored beyond a single request while still maintaining statelessness. Tokens authorize for a specified action and thus allow the delegation of authentication. Patients are identified by one or more pseudonyms and carry identifying fields. Relying on HTTP calls alone, the interface is firewall-friendly. The reference implementation has proven to be production stable.ConclusionThe RESTful pseudonymization interface fits the requirements of web-based scenarios and allows building applications that make pseudonymization transparent to the user using ordinary web technology. The open-source reference implementation implements the web interface as well as a scientifically grounded algorithm to generate non-speaking pseudonyms.

Record linkage deals with detecting homonyms and mainly synonyms in data. The package RecordLinkage provides means to perform and evaluate different record linkage methods. A stochastic framework is implemented which calculates weights through an EM algorithm. The determination of the necessary thresholds in this model can be achieved by tools of extreme value theory. Furthermore, machine learning methods are utilized, including decision trees (rpart), bootstrap aggregating (bagging), ada boost (ada), neural nets (nnet) and support vector machines (svm). The generation of record pairs and comparison patterns from single data items are provided as well. Comparison patterns can be chosen to be binary or based on some string metrics. In order to reduce computation time and memory usage, blocking can be used. Future development will concentrate on additional and refined methods, performance improvements and input/output facilities needed for real-world application.

Cleansing data from synonyms and homonyms is a relevant task in fields where high quality of data is crucial, for example in disease registries and medical research networks. Record linkage provides methods for minimizing synonym and homonym errors thereby improving data quality. We focus our attention to the case of homonym errors (in the following denoted as 'false matches'), in which records belonging to different entities are wrongly classified as equal. Synonym errors ('false non-matches') occur when a single entity maps to multiple records in the linkage result. They are not considered in this study because in our application domain they are not as crucial as false matches. False match rates are frequently computed manually through a clerical review, so without modelling the distribution of the false match rates a priori. An exception is the work of Belin and Rubin (1995) [4]. They propose to estimate the false match rate by means of a normal mixture model that needs training data for a calibration process. In this paper we present a new approach for estimating the false match rate within the framework of Fellegi and Sunter by methods of Extreme Value Theory (EVT). This approach needs no training data for determining the threshold for matches and therefore leads to a significant cost-reduction. After giving two different definitions of the false match rate, we present the tools of the EVT used in this paper: the generalized Pareto distribution and the mean excess plot. Our experiments with real data show that the model works well, with only slightly lower accuracy compared to a procedure that has information about the match status and that maximizes the accuracy.

The results support the ad-hoc solution for missing values 'replace NA by the value of inequality'. This conclusion is based on a limited amount of data and on a specific deduplication method. Nevertheless, the authors are confident that their results should be confirmed by other empirical analyses and applications.

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