6 citations · 10 across the 6 of their papers we have counts for
6 papers
How to measure intra-physician variability in clinical decision-making?
Alaedine Benani, Pierre Meneton, Emmanuel Messas +6
Intra-physician prescribing variability, the probability that one physician issues discordant decisions for two patients deemed comparable on observed covariates, holds great impac…
Development of the user-friendly decision aid Rule-based Evaluation and Support Tool (REST) for optimizing the resources of an information extraction task
Guillaume Bazin, Xavier Tannier, Fanny Adda +3
Rules could be an information extraction (IE) default option, compared to ML and LLMs in terms of sustainability, transferability, interpretability, and development burden. We sugg…
Impact of translation on biomedical information extraction from real-life clinical notes
Christel Gérardin, Yuhan Xiong, Perceval Wajsbürt +2
The objective of our study is to determine whether using English tools to extract and normalize French medical concepts on translations provides comparable performance to French mo…
Detecting automatically the layout of clinical documents to enhance the performances of downstream natural language processing
Christel Gérardin, Perceval Wajsbürt, Basile Dura +4
Objective:Develop and validate an algorithm for analyzing the layout of PDF clinical documents to improve the performance of downstream natural language processing tasks. Materials…
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records
Basile Dura, Charline Jean, Xavier Tannier +4
Background Clinical studies using real-world data may benefit from exploiting clinical reports, a particularly rich albeit unstructured medium. To that end, natural language proces…
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
Adrian Ahne, Vivek Khetan, Xavier Tannier +6
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a to…