336 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of AmsterdamNL35 papers
- Vrije Universiteit AmsterdamNL22 papers
- Amsterdam NeuroscienceNL12 papers
- University College LondonGB10 papers
- Delft University of TechnologyNL9 papers
- Leiden University Medical CenterNL9 papers
- Erasmus MCNL8 papers
- Radboud University NijmegenNL8 papers
- University Medical Center UtrechtNL8 papers
- Leiden UniversityNL7 papers
- Erasmus University RotterdamNL5 papers
- KU LeuvenBE5 papers
7 papers · 1 filter
Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients
Ana Esponera, Giovanni Cinà
Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge encoded in causal models, but their…
LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison
Gabriele De Vito, Filomena Ferrucci, Athanasios Angelakis
The increasing volume of drug combinations in modern therapeutic regimens needs reliable methods for predicting drug-drug interactions (DDIs). While Large Language Models (LLMs) ha…
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
Héctor Cadavid, Hyunho Mo, Bauke Arends +5
Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework t…
Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift
Miguel Rios, Ameen Abu-Hanna
Neural models, with their ability to provide novel representations, have shown promising results in prediction tasks in healthcare. However, patient demographics, medical technolog…
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
Lukas Galke, Iacopo Vagliano, Benedikt Franke +3
Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelo…
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
Ahmed El-Gazzar, Rajat Mani Thomas, Guido van Wingen
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popu…
Graph Space Embedding
João Pereira, Albert Groen, Erik Stroes +1
We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide th…