We are delighted to announce the successful completion of PART A of the COST Action NEUTRO-NARPS Training School, “Machine Learning Approaches for Hematological Disorder Diagnostics and Prognostics”, held on 27–28 May 2026.
The training school was designed to equip researchers with the knowledge and practical skills required to apply Machine Learning methodologies to hematological disorders, supporting improved diagnosis, prognosis, treatment stratification, and ultimately better patient outcomes.
The programme featured expert-led lectures and practical training sessions:
- Dr Gabriel Alexander Vignolle (UCLA) presented the foundations of Machine Learning in Hematology, covering the complete ML workflow from data preprocessing and feature engineering to model selection, interpretation of predictive models in clinical practice, and ethical considerations in AI-driven healthcare.
- Dr Andrea Cappozzo (Università Cattolica del Sacro Cuore, Milan) introduced advanced methodologies for analysing complex biomedical data, including supervised and unsupervised learning, mixed-effects modelling for multicentric studies, penalized estimation techniques for high-dimensional datasets, and synthetic applications in DNA methylation biomarker development.
- Dr. Georgios Manikis (Hellenic Mediterranean University), who led a hands-on training session, providing participants with practical experience of different machine learning methodological scenarios using synthetic datasets in the context of KNIME.
We warmly thank all trainers, presenters, and participants for their active engagement and valuable contributions. Special appreciation goes to the WG3 leadership team for their excellent organization and commitment to delivering a high-quality training experience.
By strengthening expertise in data analytics, statistics, and artificial intelligence for rare blood diseases, NEUTRO-NARPS continues to support the development of innovative research approaches and foster collaboration across the international scientific community.
More information: https://neutro-narps.eu/ml-hematology/
