Our COST Action CA24124 “Network for the Advancement of Neutropenia Research and Patient Support” (Neutro-NARPS) is pleased to announce a new virtual Training School on “Machine Learning Approaches & Analytical Methodologies for Hematological Disorder Diagnostics and Prognostics.”

This Training School aims to equip researchers, clinicians, and healthcare professionals with essential skills in data analytics, machine learning, and statistical methodologies, with a special focus on rare hematological disorders such as neutropenia. Addressing the challenges of small patient populations and fragmented data, the program highlights how Real World Data, artificial intelligence, and advanced statistical approaches can support better diagnosis, disease understanding, and clinical decision-making.

The Training School will take place in two distinct parts:

Part A (27–28 May 2026) focuses on Machine Learning approaches in hematology, introducing participants to key ML concepts and their application in diagnosis, prognosis, and treatment stratification. The sessions combine theory with hands-on experience using real-world datasets.

Part B (03–04 June 2026) is dedicated to Analytical Methodologies, offering practical training in statistical analysis tailored to rare and complex blood disorders, including survival analysis techniques and working with small datasets.

Participants will benefit from interactive lectures by experts, practical exercises, and opportunities to engage in discussions on real research challenges. The Training School is open to a broad audience, including researchers, clinicians, biologists, data scientists, students, and non-experts in biostatistics.

Application deadline: April 27, 2026

Interested applicants are invited to learn more and submit their application through the official page:
https://neutro-narps.eu/ml-hematology/

Don’t miss this opportunity to enhance your skills in cutting-edge analytical approaches and contribute to advancing research and patient care in rare hematological diseases.

Organizers
Dr Helen Latsoudis (WG-3 Leader)
Dr Giacomo Cavalca (WG-3 Co-Leader)