Semi-Automatic de-Identification of Hospital Discharge Summaries with Natural Language Processing. A Case-Study of Performance and Real-World Usability - Naver Labs Europe
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Patient medical records represent a very rich and important source of information for clinical research. Still, this data cannot be used directly for research purposes, as these documents contain highly-sensitive personal information protected by the law. In this paper, we evaluate the qualitative and quantitative impact of a semi-automated system (combining NLP processing, ML models and a dedicated UI) when used by human annotators for de-identifying French Hospital Discharge Summaries.

NAVER LABS Europe
NAVER LABS Europe
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