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Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach

Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach

Shweta Yadav, Asif Ekbal, Sriparna Saha, Parth S. Pathak, Pushpak Bhattacharyya
Copyright: © 2017 |Pages: 20
ISBN13: 9781522524984|ISBN10: 1522524983|EISBN13: 9781522524991
DOI: 10.4018/978-1-5225-2498-4.ch011
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MLA

Yadav, Shweta, et al. "Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach." Handbook of Research on Applied Cybernetics and Systems Science, edited by Snehanshu Saha, et al., IGI Global, 2017, pp. 234-253. https://doi.org/10.4018/978-1-5225-2498-4.ch011

APA

Yadav, S., Ekbal, A., Saha, S., Pathak, P. S., & Bhattacharyya, P. (2017). Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach. In S. Saha, A. Mandal, A. Narasimhamurthy, S. V, & S. Sangam (Eds.), Handbook of Research on Applied Cybernetics and Systems Science (pp. 234-253). IGI Global. https://doi.org/10.4018/978-1-5225-2498-4.ch011

Chicago

Yadav, Shweta, et al. "Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach." In Handbook of Research on Applied Cybernetics and Systems Science, edited by Snehanshu Saha, et al., 234-253. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2498-4.ch011

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Abstract

With the rapid increment in the clinical text, de-identification of patient Protected Health Information (PHI) has drawn significant attention in recent past. This aims for automatic identification and removal of the patient Protected Health Information from medical records. This paper proposes a supervised machine learning technique for solving the problem of patient data de- identification. In the current paper, we provide an insight into the de-identification task, its major challenges, techniques to address challenges, detailed analysis of the results and direction of future improvement. We extract several features by studying the properties of the datasets and the domain. We build our model based on the 2014 i2b2 (Informatics for Integrating Biology to the Bedside) de-identification challenge. Experiments show that the proposed system is highly accurate in de-identification of the medical records. The system achieves the final recall, precision and F-score of 95.69%, 99.31%, and 97.46%, respectively.

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