Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients

BACKGROUND: Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890696/
https://www.ncbi.nlm.nih.gov/pubmed/35252956
http://dx.doi.org/10.3389/fdgth.2022.728922

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890696/
https://www.ncbi.nlm.nih.gov/pubmed/35252956
http://dx.doi.org/10.3389/fdgth.2022.728922

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