State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework | Zendy

György Szarvas | Zendy; Richárd Farkas | Zendy; Róbert BusaFekete | Zendy

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State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework

Author(s) -

György Szarvas,

Richárd Farkas,

Róbert BusaFekete

Publication year - 2007

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/j.jamia.m2441

Subject(s) - computer science , software portability , health insurance portability and accountability act , security token , identification (biology) , protected health information , named entity recognition , precision and recall , artificial intelligence , information retrieval , unified medical language system , recall , medical record , measure (data warehouse) , machine learning , data mining , natural language processing , confidentiality , public health , programming language , computer security , medicine , radiology , philosophy , health promotion , hrhis , task (project management) , linguistics , biology , management , botany , nursing , economics

The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act.

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