Publications by authors named "R Bey"
Article Synopsis
- The study aimed to create and validate a natural language processing (NLP) pipeline capable of identifying 18 medical conditions in French clinical notes, including various comorbidities from the Charlson index, while ensuring privacy in a collaborative research environment.
- The detection pipeline employed both rule-based and machine learning techniques, utilizing a large language model and annotated clinical notes from three research studies focused on oncology, cardiology, and rheumatology.
- Results showed high accuracy metrics, including a macro-averaged F1-score of 95.7, indicating that the collaborative effort significantly outperformed other methods, demonstrating the effectiveness of secure teamwork in developing advanced medical AI models.
Objective: The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse.
Methods: We annotated a corpus of clinical documents according to 12 types of identifying entities and built a hybrid system, merging the results of a deep learning model as well as manual rules.
Objectives: Medico-administrative data are promising to automate the calculation of Healthcare Quality and Safety Indicators. Nevertheless, not all relevant indicators can be calculated with this data alone. Our feasibility study objective is to analyze 1) the availability of data sources; 2) the availability of each indicator elementary variables, and 3) to apply natural language processing to automatically retrieve such information.
View Article and Find Full Text PDFBackground: The SARS CoV-2 pandemic disrupted healthcare systems. We compared the cancer stage for new breast cancers (BCs) before and during the pandemic.
Methods: We performed a retrospective multicenter cohort study on the data warehouse of Greater Paris University Hospitals (AP-HP).