Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.

Objective: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.

Methods: We focused on events related to cardiac catheterization as defined by the NCDR-IMPACT registry. These events were extracted from the Necker Children's Hospital data warehouse.

Real-life implementation and evaluation of the e-referral system SIPILINK.

Introduction: General Practitioners (GPs) play a key role of gatekeeper, as they coordinate patients' care. However, most of them reported having difficulty to refer patients to hospital, especially in semi-urgent context. To facilitate the referral of semi-urgent patients, we implemented an e-referral platform, named SIPILINK, within 4 wards from a large public French hospital (internal medicine, diabetology, gynaecological surgery and oncology wards).

Evaluation of BERT-Based Models on Patient Data from French Social Media.

With the objective of extracting new knowledge about rare diseases from social media messages, we evaluated three models on a Named Entity Recognition (NER) task, consisting of extracting phenotypes and treatments from social media messages. We trained the three models on a dataset with social media messages about Developmental and Epileptic Encephalopathies and more common diseases. This preliminary study revealed that CamemBERT and CamemBERT-bio exhibit similar performance on social media testimonials, slightly outperforming DrBERT.

An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse.

Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification.

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model.