Mohamed Azzouzi- University of Rennes
Mohamed Azzouzi
- University of Rennes
About
6
Publications
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Introduction
Current institution
Publications
Publications (6)
BACKGROUND
Clinical data warehouses store large amounts of patient information in the form of unstructured text, from which medical status can be extracted using natural language processing (NLP) for research. Current machine learning-based extraction systems use the named entity recognition (NER) approach, requiring lots of manual annotation by me...
Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving...
BACKGROUND
The digital transformation of health data has enabled the utilization of advanced data analytics and Artificial Intelligence (AI) techniques, which are crucial for driving innovation in healthcare. Countries such as France, the UK, Germany, and the US have adopted strategies to secure and ethically manage health data. In France, the Heal...
Background
The European Union’s General Data Protection Regulation (GDPR) has profoundly influenced health data management, with significant implications for clinical data warehouses (CDWs). In 2021, France pioneered a national framework for GDPR-compliant CDW implementation, established by its data protection authority (Commission Nationale de l’I...
Background
Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods ha...
Background: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. Deidentification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods ha...
