Liesbet M. Peeters

Marcel Parciak, Brecht Vandevoort, Frank Neven et al.

Large Language Models (LLMs) have shown useful applications in a variety of tasks, including data wrangling. In this paper, we investigate the use of an off-the-shelf LLM for schema matching. Our objective is to identify semantic correspondences between elements of two relational schemas using only names and descriptions. Using a newly created benchmark from the health domain, we propose different so-called task scopes. These are methods for prompting the LLM to do schema matching, which vary in the amount of context information contained in the prompt. Using these task scopes we compare LLM-based schema matching against a string similarity baseline, investigating matching quality, verification effort, decisiveness, and complementarity of the approaches. We find that matching quality suffers from a lack of context information, but also from providing too much context information. In general, using newer LLM versions increases decisiveness. We identify task scopes that have acceptable verification effort and succeed in identifying a significant number of true semantic matches. Our study shows that LLMs have potential in bootstrapping the schema matching process and are able to assist data engineers in speeding up this task solely based on schema element names and descriptions without the need for data instances.

Ashkan Pirmani, Ilse Vermeulen, Goran Vinterhalter et al.

Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but the practical barrier now comes earlier: a team starting a federated project meets a scattered mix of frameworks, governance obligations, and unfamiliar roles, with no structured place to begin that fits its own background. FLKit closes that gap. It is an open, community-maintained onboarding toolkit that takes a multidisciplinary team through the full federated learning lifecycle and gives every contributor, clinical, legal, governance, or technical, a role-aware entry point instead of assuming fluency across all four. We modeled it on the ELIXIR Research Data Management Kit and built it with a multidisciplinary core team, a wider consortium supplying milestone reviews and roadmap direction, and external practitioners interviewed to keep the content grounded in real practice. FLKit sits on four lifecycle stages, Governance, Infrastructure, Wrangling, and Analysis, and connects them through 11 role-specific entry points, a cross-disciplinary glossary, a reusable FAIR-aligned FL Story template for planning and documenting projects, and a curated directory of tools, frameworks, and communities. Since the December 2024 demo it has grown to 39 pages across eight sections, with seven FL Stories documenting completed and ongoing projects in multiple sclerosis disability prediction, inflammatory bowel disease, genomics, and brain-computer interfaces. It is openly available at https://uhasselt-biomedicaldatasciences.github.io/federated-learning-toolkit/ and welcomes contributions from across the life sciences.

Axel Faes, Ashkan Pirmani, Yves Moreau et al.

Block-Term Tensor Regression (BTTR) has proven to be a powerful tool for modeling complex, high-dimensional data by leveraging multilinear relationships, making it particularly well-suited for applications in healthcare and neuroscience. However, traditional implementations of BTTR rely on centralized datasets, which pose significant privacy risks and hinder collaboration across institutions. To address these challenges, we introduce Federated Block-Term Tensor Regression (FBTTR), an extension of BTTR designed for federated learning scenarios. FBTTR enables decentralized data analysis, allowing institutions to collaboratively build predictive models while preserving data privacy and complying with regulations. FBTTR represents a major step forward in applying tensor regression to federated learning environments. Its performance is evaluated in two case studies: finger movement decoding from Electrocorticography (ECoG) signals and heart disease prediction. In the first case study, using the BCI Competition IV dataset, FBTTR outperforms non-multilinear models, demonstrating superior accuracy in decoding finger movements. For the dataset, for subject 3, the thumb obtained a performance of 0.76 $\pm$ .05 compared to 0.71 $\pm$ 0.05 for centralised BTTR. In the second case study, FBTTR is applied to predict heart disease using real-world clinical datasets, outperforming both standard federated learning approaches and centralized BTTR models. In the Fed-Heart-Disease Dataset, an AUC-ROC was obtained of 0.872 $\pm$ 0.02 and an accuracy of 0.772 $\pm$ 0.02 compared to 0.812 $\pm$ 0.003 and 0.753 $\pm$ 0.007 for the centralized model.

Edward De Brouwer, Thijs Becker, Yves Moreau et al.

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.