DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text |
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| Authors: | Vincent Menger Floor Scheepers Lisette Maria van Wijk Marco Spruit |
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| Affiliation: | 1. Department of Information and Computing Sciences, Utrecht University, P.O. Box 80089, 3508 TB Utrecht, The Netherlands;2. Department of Psychiatry, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands |
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| Abstract: | In order to use medical text for research purposes, it is necessary to de-identify the text for legal and privacy reasons. We report on a pattern matching method to automatically de-identify medical text written in Dutch, which requires a low amount of effort to be hand tailored. First, a selection of Protected Health Information (PHI) categories is determined in cooperation with medical staff. Then, we devise a method for de-identifying all information in one of these PHI categories, that relies on lookup tables, decision rules and fuzzy string matching. Our de-identification method DEDUCE is validated on a test corpus of 200 nursing notes and 200 treatment plans obtained from the University Medical Center Utrecht (UMCU) in the Netherlands, achieving a total micro-averaged precision of 0.814, a recall of 0.916 and a F1-score of 0.862. For person names, a recall of 0.964 was achieved, while no names of patients were missed. |
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| Keywords: | De-identification Dutch medical text Pattern matching Protected Health Information Patient privacy |
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