Publications by authors named "C Gerardin"
Controlling the structure and functionality of porous silica nanoparticles has been a continuous source of innovation with important potential for advanced biomedical applications. Their synthesis, however, usually involves passive surfactants or amphiphilic copolymers that do not add value to the material after synthesis. In contrast, polyion complex (PIC) micelles based on hydrophilic block copolymers allow for the direct synthesis of intrinsically functional hybrid materials.
View Article and Find Full Text PDFBackground: Biomedical natural language processing tasks are best performed with English models, and translation tools have undergone major improvements. On the other hand, building annotated biomedical data sets remains a challenge.
Objective: The aim of our study is to determine whether the use of English tools to extract and normalize French medical concepts based on translations provides comparable performance to that of French models trained on a set of annotated French clinical notes.
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.
Background: Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France.
View Article and Find Full Text PDFBackground: Reliable and interpretable automatic extraction of clinical phenotypes from large electronic medical record databases remains a challenge, especially in a language other than English.
Objective: We aimed to provide an automated end-to-end extraction of cohorts of similar patients from electronic health records for systemic diseases.
Methods: Our multistep algorithm includes a named-entity recognition step, a multilabel classification using medical subject headings ontology, and the computation of patient similarity.