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Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients.
Paul, Tanmoy; Rana, Md Kamruz Zaman; Tautam, Preethi Aishwarya; Kotapati, Teja Venkat Pavan; Jampani, Yaswitha; Singh, Nitesh; Islam, Humayera; Mandhadi, Vasanthi; Sharma, Vishakha; Barnes, Michael; Hammer, Richard D; Mosa, Abu Saleh Mohammad.
Afiliación
  • Paul T; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.
  • Rana MKZ; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.
  • Tautam PA; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.
  • Kotapati TVP; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.
  • Jampani Y; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.
  • Singh N; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.
  • Islam H; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.
  • Mandhadi V; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.
  • Sharma V; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.
  • Barnes M; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.
  • Hammer RD; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.
  • Mosa ASM; Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States.
Front Digit Health ; 4: 728922, 2022.
Article en En | MEDLINE | ID: mdl-35252956
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Revista: Front Digit Health Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Revista: Front Digit Health Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza