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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
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 (...
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 |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
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 |
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