Search Results for author: Adrien Coulet

Found 9 papers, 3 papers with code

Investigating ADR mechanisms with knowledge graph mining and explainable AI

no code implementations16 Dec 2020 Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, Adrien Coulet

We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR.

Explainable Models Graph Mining +1

Discovering alignment relations with Graph Convolutional Networks: a biomedical case study

1 code implementation11 Nov 2020 Pierre Monnin, Chedy Raïssi, Amedeo Napoli, Adrien Coulet

In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.

Knowledge Graphs

Knowledge-Based Matching of $n$-ary Tuples

1 code implementation19 Feb 2020 Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet

In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.

Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

no code implementations12 Sep 2018 Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies.

Fairness Management

Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text

no code implementations ICLR 2018 Legrand Joël, Yannick Toussaint, Chedy Raïssi, Adrien Coulet

Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences).

Cross-corpus Relationship Extraction (Distant Supervised)

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