22 citations · 52 across the 7 of their papers we have counts for
7 papers
Autoencoder-based prediction of ICU clinical codes
Tsvetan R. Yordanov, Ameen Abu-Hanna, Anita CJ Ravelli +1
Availability of diagnostic codes in Electronic Health Records (EHRs) is crucial for patient care as well as reimbursement purposes. However, entering them in the EHR is tedious, an…
Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes
Auke Elfrink, Iacopo Vagliano, Ameen Abu-Hanna +1
We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-tex…
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…
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities
Iacopo Vagliano, Lukas Galke, Ansgar Scherp
We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject l…
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
Lukas Galke, Florian Mai, Iacopo Vagliano +1
We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze th…
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
Lukas Galke, Iacopo Vagliano, Ansgar Scherp
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising,…
Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
Iacopo Vagliano, Diego Monti, Ansgar Scherp +1
Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users…