7 citations · 24 across the 9 of their papers we have counts for
9 papers
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
Lillian Muyama, Estelle Lu, Geoffrey Cheminet +5
Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health record…
Facilitating phenotyping from clinical texts: the medkit library
Antoine Neuraz, Ghislain Vaillant, Camila Arias +8
Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic…
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
Lillian Muyama, Antoine Neuraz, Adrien Coulet
Background: Clinical diagnosis is typically reached by following a series of steps recommended by guidelines authored by colleges of experts. Accordingly, guidelines play a crucial…
The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials
Sophie Quennelle, Maxime Douillet, Lisa Friedlander +4
In medical research, the traditional way to collect data, i.e. browsing patient files, has been proven to induce bias, errors, human labor and costs. We propose a semi-automated sy…
Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning
Lillian Muyama, Antoine Neuraz, Adrien Coulet
Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis. Inspired by guidelines, we aim to learn the optimal sequence of actions to perform in order…
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…
Learning the grammar of drug prescription: recurrent neural network grammars for medication information extraction in clinical texts
Ivan Lerner, Jordan Jouffroy, Anita Burgun +1
In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts. We evaluated this model on a French clinic…
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context
Antoine Neuraz, Leonardo Campillos Llanos, Anita Burgun +1
In the biomedical domain, the lack of sharable datasets often limit the possibility of developing natural language processing systems, especially dialogue applications and natural…
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière +3
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases. Our goal is to achieve a state of knowle…