Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies la...

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Bibliographic Details
Main Authors: Tanmoy Paul, Humayera Islam, Nitesh Singh, Yaswitha Jampani, Teja Venkat Pavan Kotapati, Preethi Aishwarya Tautam, Md Kamruz Zaman Rana, Vasanthi Mandhadi, Vishakha Sharma, Michael Barnes, Richard D. Hammer, Abu Saleh Mohammad Mosa
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Applied Sciences
Subjects:
protected health information
natural language processing (NLP)
named entity recognition (NER)
de-identification
conditional random field (CRF)
Online Access:https://www.mdpi.com/2076-3417/12/19/9976

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