Neutro-NARPS Training School on Machine Learning Approaches & Analytical Methodologies for Hematological Disorder Diagnostics and Prognostics
Online, 27-28 May & 03-04 June; https://neutro-narps.eu/ml-hematology/
The Training School on "Machine Learning Approaches & Analytical Methodologies for Hematological Disorder Diagnostics and Prognostics" is organized by the COST Action CA24124 "Network for the Advancement of Neutropenia Research and Patient Support" (Neutro-NARPS) as a virtual event.
Data analytics methods in rare diseases are crucial for overcoming challenges related to small patient populations, data fragmentation, and the "diagnostic odyssey". These methods leverage Real World Data, core statistics, and AI to improve diagnosis, understand disease progression, and accelerate drug development. The participating scientists will have the opportunity to expand their knowledge through interactive lectures by experts on aspects of data analytics. They will also observe and familiarize themselves with methods to study rare blood diseases, discuss, and get hands-on experience in performing specific analyses that will promote better study design, more efficient use of data, and improved interpretation of results, ultimately contributing to more informed clinical decision-making and better patient outcomes.
The Neutro-NARPS Training School will be held in 2 distinct parts, each on a separate date.
PART A: 27-28 May, 2026 on "Machine Learning Approaches for Hematological Disorder Diagnostics and Prognostics". Download Part A Agenta.
PART B: 03-04 June, 2026 on "Analytical Methodologies for Hematological Disorder Diagnostics and Prognostics". Download Part B Agenta.
May 27 – Afternoon Session (3Hrs)
14.00 – 15.30 | Foundations of Machine Learning in Hematology (Dr Gabriel Vignolle)
The talk will provide substantial information on important aspects of
- Data to Model: A Machine Learning workflow
- Data preprocessing, Feature engineering & Model selection
- Tips and Tricks: Handling small sample sizes, missingness and sparse data
- Interpretation of predictive models for clinicians, translating ML into daily patient care
- Ethical considerations and bias in ML models
15.30 – 17.00 | Machine Learning for Complex Biomedical Data" (Dr Andrea Cappozzo)
The talk will cover aspects related to
- Introduction to Machine Learning and Data Science: key concepts and taxonomy of approaches
- Supervised vs. unsupervised learning: distinctions, use cases
- Handling complexity: multicentric data with linear mixed models
- Handling complexity: high-dimensional data via penalized estimation
- Case study: DNA Methylation surrogate biomarker creation with penalized mixed-effects multitask learning
May 28 – Afternoon session (3Hrs)
14.00 – 17.00 | Hands-on training ( Dr George Manikis)
Trainees will practice on datasets, on their own electronic device, in real-time following their trainers' instructions.
TRAINERS:
Dr. Gabriel Alexander Vignolle, Mag.pharm. Dr.rer.nat.Postdoctoral Scholar
– The Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, USA
– Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
– Goodman-Luskin Microbiome Center, UCLA, Los Angeles, CA, USA
Dr. Andrea Cappozzo,
– Associate Professor of Statistics, Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
Dr Georgios Manikis, PhD, Electronic Engineer
– Associate Professor, Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, Greece
– Collaborating researcher, Computational Biomedicine Laboratory(CBML), Institute of Computer Sciences(ICS), Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
June 3 – Afternoon Session (3Hrs)
14.00 – 15.30 | Survival analysis of time-to-event data: basic concepts and methods" (Prof. Evangelos Kritsotakis)
This session will cover essential concepts and methods for analyzing time-to-event data or survival times. It will explain the special features of survival data, censoring, and the basic mathematical functions central to describing survival times (the survival, hazard rate, and cumulative hazard functions) and their interrelationships. Basic methods such as the non-parametric Kaplan–Meier method to estimate survival probabilities, the log-rank and Wilcoxon-Peto-Peto tests for comparing survival time distributions between two or more groups will be briefly presented and illustrated. We will also discuss the formulation and application of the Cox proportional hazards model for regression analysis.
15.30 – 17.00 | Application of Bayesian Methods in Hematology (Prof. Esin Avci)
The main aim of this talk is to introduce clinicians to the practical applications of Bayesian methods in medicine. Rather than emphasizing mathematical details, the presentation will highlight how these approaches can better understand patient outcomes, incorporate prior clinical knowledge, and make more informed decisions under uncertainty. Through examples, the talk will demonstrate how Bayesian methods can improve the analysis of time-to-event data and model complex relationships between risk factors. By the end of the session, participants will gain an intuitive understanding of how prior information and different types of clinical data can be integrated to support more personalized and evidence-based medical decision-making.
June 4 Hands-on training
Speakers/Trainers:
Prof. Evangelos I. Kritsotakis, BSc(Hons) MSc PhD CStat FHEA MRSPH, Associate Professor of Biostatistics
– Division of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
Prof Esin Avci, Associate Professor of Statistics
– Department of Statistics, Faculty of Arts and Sciences, Giresun University, Giresun, Türkiye