Formal verification and complexity analysis of confidentiality aware textual clinical documents framework

Author:

Kanwal Tehsin1ORCID,Moqurrab Syed A.12,Anjum Adeel1ORCID,Khan Abid3ORCID,Rodrigues Joel J. P. C.45ORCID,Jeon Gwanggil67ORCID

Affiliation:

1. Department of Computer Science COMSATS University Islamabad Islamabad Pakistan

2. Department of Computer Science Air University Islamabad Islamabad Pakistan

3. Department of Computer Science Aberystwyth University Aberystwyth UK

4. Graduate Program in Electrical Engineering Federal University of Piauí Teresina Brazil

5. Instituto de Telecomunicações Covilhã Portugal

6. School of Electronic Engineering Xidian University Xi'an China

7. Department of Embedded Systems Engineering Incheon National University Incheon Korea

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

National Research Foundation of Korea

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Reference43 articles.

1. N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets

2. Entity recognition from clinical texts via recurrent neural network

3. RuchP BaudRH RassinouxAM BouillonP RobertG. Medical document anonymization with a semantic lexicon. In:Proceedings of AMIA Symposium;2000:729‐733 [Online].http://www.ncbi.nlm.nih.gov/pubmed/11079980. Accessed April 12 2019.

4. Injecting purpose and trust into data anonymisation

5. An overview of MetaMap: historical perspective and recent advances

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