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Journal Abstract Search

223 related items for PubMed ID: 23920600

  • 1.
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  • 2. De-identification of clinical notes in French: towards a protocol for reference corpus development.
    Grouin C, Névéol A.
    J Biomed Inform; 2014 Aug; 50():151-61. PubMed ID: 24380818
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  • 4. Proposal and evaluation of FASDIM, a Fast And Simple De-Identification Method for unstructured free-text clinical records.
    Chazard E, Mouret C, Ficheur G, Schaffar A, Beuscart JB, Beuscart R.
    Int J Med Inform; 2014 Apr; 83(4):303-12. PubMed ID: 24370391
    [Abstract] [Full Text] [Related]

  • 5. Automatic detection of protected health information from clinic narratives.
    Yang H, Garibaldi JM.
    J Biomed Inform; 2015 Dec; 58 Suppl(Suppl):S30-S38. PubMed ID: 26231070
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  • 7. Extracting important information from Chinese Operation Notes with natural language processing methods.
    Wang H, Zhang W, Zeng Q, Li Z, Feng K, Liu L.
    J Biomed Inform; 2014 Apr; 48():130-6. PubMed ID: 24486562
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  • 10. CRFs based de-identification of medical records.
    He B, Guan Y, Cheng J, Cen K, Hua W.
    J Biomed Inform; 2015 Dec; 58 Suppl(Suppl):S39-S46. PubMed ID: 26315662
    [Abstract] [Full Text] [Related]

  • 11. Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.
    Gobbel GT, Reeves R, Jayaramaraja S, Giuse D, Speroff T, Brown SH, Elkin PL, Matheny ME.
    J Biomed Inform; 2014 Apr; 48():54-65. PubMed ID: 24316051
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  • 12. An efficient pancreatic cyst identification methodology using natural language processing.
    Mehrabi S, Schmidt CM, Waters JA, Beesley C, Krishnan A, Kesterson J, Dexter P, Al-Haddad MA, Tierney WM, Palakal M.
    Stud Health Technol Inform; 2013 Apr; 192():822-6. PubMed ID: 23920672
    [Abstract] [Full Text] [Related]

  • 13. A machine learning based approach to identify protected health information in Chinese clinical text.
    Du L, Xia C, Deng Z, Lu G, Xia S, Ma J.
    Int J Med Inform; 2018 Aug; 116():24-32. PubMed ID: 29887232
    [Abstract] [Full Text] [Related]

  • 14. Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.
    Ferrández O, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM.
    BMC Med Res Methodol; 2012 Jul 27; 12():109. PubMed ID: 22839356
    [Abstract] [Full Text] [Related]

  • 15. De-identification of unstructured paper-based health records for privacy-preserving secondary use.
    Fenz S, Heurix J, Neubauer T, Rella A.
    J Med Eng Technol; 2014 Jul 27; 38(5):260-8. PubMed ID: 24841844
    [Abstract] [Full Text] [Related]

  • 16. Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records.
    Grouin C, Moriceau V, Zweigenbaum P.
    J Biomed Inform; 2015 Dec 27; 58 Suppl(Suppl):S133-S142. PubMed ID: 26142870
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  • 17. Building a common pipeline for rule-based document classification.
    Patterson OV, Ginter T, DuVall SL.
    Stud Health Technol Inform; 2013 Dec 27; 192():1211. PubMed ID: 23920985
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  • 18. Automatic extracting of patient-related attributes: disease, age, gender and race.
    Zhu H, Ni Y, Cai P, Qiu Z, Cao F.
    Stud Health Technol Inform; 2012 Dec 27; 180():589-93. PubMed ID: 22874259
    [Abstract] [Full Text] [Related]

  • 19. De-identifying free text of Japanese electronic health records.
    Kajiyama K, Horiguchi H, Okumura T, Morita M, Kano Y.
    J Biomed Semantics; 2020 Sep 21; 11(1):11. PubMed ID: 32958039
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  • 20. A hybrid model for automatic identification of risk factors for heart disease.
    Yang H, Garibaldi JM.
    J Biomed Inform; 2015 Dec 21; 58 Suppl(Suppl):S171-S182. PubMed ID: 26375492
    [Abstract] [Full Text] [Related]

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