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Volumn 58, Issue , 2015, Pages S39-S46

CRFs based de-identification of medical records

Author keywords

Conditional random fields; De identification; Medical records; Protected health information

Indexed keywords

BIOINFORMATICS;

EID: 84940726529     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.08.012     Document Type: Article
Times cited : (44)

References (13)
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    • Uzuner, Ö.1    Luo, Y.2    Szolovits, P.3
  • 3
    • 77955080124 scopus 로고    scopus 로고
    • Automatic de-identification of textual documents in the electronic health record: a review of recent research
    • Meystre S.M., Friedlin F.J., South B.R., Shen S., Samore M.H. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med. Res. Methodol. 2010, 10:70.
    • (2010) BMC Med. Res. Methodol. , vol.10 , pp. 70
    • Meystre, S.M.1    Friedlin, F.J.2    South, B.R.3    Shen, S.4    Samore, M.H.5
  • 5
    • 84904305048 scopus 로고    scopus 로고
    • De-identification of health records using Anonym: effectiveness and robustness across datasets
    • Zuccon G., Kotzur D., Nguyen A., Bergheim A. De-identification of health records using Anonym: effectiveness and robustness across datasets. Artif. Intell. Med. 2014, 61:145-151.
    • (2014) Artif. Intell. Med. , vol.61 , pp. 145-151
    • Zuccon, G.1    Kotzur, D.2    Nguyen, A.3    Bergheim, A.4
  • 7
    • 84963738282 scopus 로고    scopus 로고
    • De-identifying health records by means of active learning
    • Boström H., Dalianis H. De-identifying health records by means of active learning. Recall (micro) 2012, 97(97).
    • (2012) Recall (micro) , vol.97 , Issue.97
    • Boström, H.1    Dalianis, H.2
  • 8
    • 34548497406 scopus 로고    scopus 로고
    • State-of-the-art anonymization of medical records using an iterative machine learning framework
    • Szarvas G., Farkas R., Busa-Fekete R. State-of-the-art anonymization of medical records using an iterative machine learning framework. J. Am. Med. Inform. Assoc. 2007, 14:574-580.
    • (2007) J. Am. Med. Inform. Assoc. , vol.14 , pp. 574-580
    • Szarvas, G.1    Farkas, R.2    Busa-Fekete, R.3
  • 9
    • 84948823441 scopus 로고    scopus 로고
    • Practical applications for NLP in clinical research: The 2014 i2b2/UTHealth shared tasks,
    • A. Stubbs, C. Kotfila, H. Xu, Ö. Uzuner, Practical applications for NLP in clinical research: The 2014 i2b2/UTHealth shared tasks, J. Biomed. Inform. 58S (2015) S1-S5.
    • (2015) J. Biomed. Inform. , vol.58S , pp. S1-S5
    • Stubbs, A.1    Kotfila, C.2    Xu, H.3    Uzuner, Ö.4
  • 10
    • 84951026409 scopus 로고    scopus 로고
    • Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
    • A. Stubbs, Ö. Uzuner, Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus, J. Biomed. Inform. 58S (2015) S20-S29.
    • (2015) J. Biomed. Inform. , vol.58S , pp. S20-S29
    • Stubbs, A.1    Uzuner, Ö.2
  • 11
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    • Transfer learning based clinical concept extraction on data from multiple sources
    • Lv X., Guan Y., Deng B. Transfer learning based clinical concept extraction on data from multiple sources. J. Biomed. Inform. 2014, 52:55-64.
    • (2014) J. Biomed. Inform. , vol.52 , pp. 55-64
    • Lv, X.1    Guan, Y.2    Deng, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.