6th CEN Conference

Session

Machine learning and data science 4

21 May 2026, 15:45

Room 14

Conveners

Machine learning and data science 4

• Andreas Ziegler (Cardio-CARE)

203. Generative Adversarial Networks For Mortality Modelling

Łukasz Głąb (SGH Warsaw School of Economics)

21/05/2026, 15:45

oral presentation

Mortality risk modeling and forecasting is one of the key tasks of social security institutions and insurance companies. Traditionally used stochastic mortality models, such as the Lee-Carter model, require fulfillment of formal assumptions that cannot always be met in real-life scenarios (e.g. time independence of age-specific improvement rates). Alternative approaches are based on deep...

85. Lesion Network Mapping based Convolutional Neural Networks: Predictive Performance and Interpretability

Matthias Becher (Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institut für Biometrie und klinische Epidemiologie, Germany)

21/05/2026, 16:03

oral presentation

Stroke can lead to a wide range of symptoms including acute motor impairment, post-stroke depression or cognitive impairment. Anticipating these outcomes early on and understanding their underlying causes would enable clinicians to initiate appropriate targeted treatments, which could not only help reduce the severity of symptoms but also improve long-term recovery. Convolutional neural...

101. Deep Learning Strategies for Rare and Common Brain Disease Diagnostics from Medical Imaging

Anna Mamchych (Adam Mickiewicz University)

21/05/2026, 16:21

oral presentation

This presentation explores the statistical challenges and comparative performance of various deep learning models for the automated detection and classification of neurological diseases from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Building upon initial findings that demonstrated the potential of Convolutional Neural Networks (CNNs) in recognizing rare brain...

27. From Data to Decisions: Enhancing the Reliability of Random Forest Predictions with optRF

Thomas Martin Lange (Breeding Informatics, Georg August University of Göttingen)

21/05/2026, 16:39

oral presentation

Random forest is a widely used machine learning method across the life sciences due to its high predictive performance, minimal assumptions, and flexibility in handling diverse data types. However, a critical yet often overlooked property of random forest is its inherent non-determinism: repeated runs on the same data set can produce different prediction models. This variability can compromise...