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Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach
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Author(s): Shweta Yadav (Indian Institute of Technology Patna, India), Asif Ekbal (Indian Institute of Technology Patna, India), Sriparna Saha (Indian Institute of Technology Patna, India), Parth S. Pathak (ezDI, LLC, India)and Pushpak Bhattacharyya (Indian Institute of Technology Patna, India)
Copyright: 2020
Pages: 20
Source title:
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch075
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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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