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

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

BackgroundElectronic 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 (ML...

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Bibliographic Details
Main Authors: Tanmoy Paul, Md Kamruz Zaman Rana, Preethi Aishwarya Tautam, Teja Venkat Pavan Kotapati, Yaswitha Jampani, Nitesh Singh, Humayera Islam, Vasanthi Mandhadi, Vishakha Sharma, Michael Barnes, Richard D. Hammer, Abu Saleh Mohammad Mosa
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-02-01
Series:Frontiers in Digital Health
Subjects:
clinical text de-identification
protected health information
NLP
named entity recognition
de-identification
conditional random field
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2022.728922/full

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