activity
20192026
most citedTerminologies augmented recurrent neural network model for clinical named entity recognition

37 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2

Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, order…

cs.LG2025

Spectral structure learning for clinical time series

Ivan Lerner, Anita Burgun, Francis Bach

We develop and evaluate a structure learning algorithm for clinical time series. Clinical time series are multivariate time series observed in multiple patients and irregularly sam…

cs.CL2024

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…

cs.CL2020★ 3 cited

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…

cs.CL2019★ 37 cited

Terminologies augmented recurrent neural network model for clinical named entity recognition

Ivan Lerner, Nicolas Paris, Xavier Tannier

We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we bui…

cs.CL2019★ 2 cited

Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials

Xavier Tannier, Nicolas Paris, Hugo Cisneros +10

Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way…