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A study of deep learning methods for de-identification of clinical notes in cross-institute settings.
Yang, Xi; Lyu, Tianchen; Li, Qian; Lee, Chih-Yin; Bian, Jiang; Hogan, William R; Wu, Yonghui.
Affiliation
  • Yang X; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Lyu T; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Li Q; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Lee CY; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Bian J; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Hogan WR; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA.
  • Wu Y; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building 2004 Mowry Road, PO Box 100177, Gainesville, Florida, USA. yonghui.wu@ufl.edu.
BMC Med Inform Decis Mak ; 19(Suppl 5): 232, 2019 12 05.
Article in En | MEDLINE | ID: mdl-31801524
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Anonymization / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article Affiliation country: United States
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Anonymization / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article Affiliation country: United States