Influence of Module Order on Rule-Based De-identification of Personal Names in Electronic Patient Records Written in Swedish

Electronic patient records (EPRs) are a valuable resource for research but for confidentiality reasons they cannot be used freely. In order to make EPRs available to a wider group of researchers, sensitive information such as personal names has to be removed. Deidentification is a process that makes this possible. Both rule-based as well as statistical and machine learning based methods exist to perform de-identification, but the second method requires annotated training material which exists only very sparsely for patient names. It is therefore necessary to use rule-based methods for de-identification of EPRs. Not much is known, however, about the order in which the various rules should be applied and how the different rules influence precision and recall. This paper aims to answer this research question by implementing and evaluating four common rules for de-identification of personal names in EPRs written in Swedish: (1) dictionary name matching, (2) title matching, (3) common words filtering and (4) learning from previous modules. The results show that to obtain the highest recall and precision, the rules should be applied in the following order: title matching, common words filtering and dictionary name matching.