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Rapidly Retargetable Approaches to De-identification in Medical Records

^a The MITRE Corporation, Bedford, MA
^b Center for Biomedical Informatics, Harvard Medical School, Boston, MA
^c Department of Computer Science, Brandeis University, Waltham, MA
^d Stanford Biomedical Informatics, Palo Alto, CA.

^_* Correspondence and reprints: John Aberdeen, 202 Burlington Road, Bedford, MA 01730 (Email: aberdeen{at}mitre.org).

Received for publication: 03/13/07; accepted for publication: 06/11/07.

Objective: This paper describes a successful approach to de-identification that was developed to participate in a recent AMIA-sponsored challenge evaluation.

Method: Our approach focused on rapid adaptation of existing toolkits for named entity recognition using two existing toolkits, Carafe and LingPipe.

Results: The "out of the box" Carafe system achieved a very good score (phrase F-measure of 0.9664) with only four hours of work to adapt it to the de-identification task. With further tuning, we were able to reduce the token-level error term by over 36% through task-specific feature engineering and the introduction of a lexicon, achieving a phrase F-measure of 0.9736.

Conclusions: We were able to achieve good performance on the de-identification task by the rapid retargeting of existing toolkits. For the Carafe system, we developed a method for tuning the balance of recall vs. precision, as well as a confidence score that correlated well with the measured F-score.

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