6th CEN Conference

Session

Machine learning and data science 2

19 May 2026, 15:45

Room 14

Conveners

Machine learning and data science 2

• Elżbieta Kubera (University of Life Sciences in Lublin)

179. Supervised Machine Learning Models for Longitudinal and Clustered Data

Maxi Schulz (University Medical Center Göttingen, Department of Medical Statistics)

19/05/2026, 15:45

oral presentation

Longitudinal or clustered data often arise in clinical research, potentially violating the independent and identically distributed (i.i.d) assumption. In regression, (generalized) linear mixed-effect models are frequently used to account for the correlation structure of the data, but these come with restrictions such as the linearity assumption and pre-specification of predictors and their...

181. Can We Trust Interpretable Machine Learning Methods for Longitudinal Risk Prediction?

Julia Höpler (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Faculty of Mathematics and Computer Science – University of Bremen)

19/05/2026, 16:03

oral presentation

Machine learning (ML) models have emerged as a powerful alternative to traditional statistical methods due to their flexibility and ability to leverage large-scale, high-dimensional datasets. However, in sensitive application areas such as clinical and prognostic modeling, deploying ML models requires interpretability in order to reveal underlying model behavior, identify influential risk...

136. Flexible nonparametric bootstrapping for machine learning validation studies based on hierarchical data

Max Westphal (Fraunhofer MEVIS)

19/05/2026, 16:21

oral presentation

Introduction:
Machine learning (ML) validation studies can often be tackled with standard statistical inference methods, i.e. confidence intervals and statistical tests. While this is reasonable in many situations there are also conditions under which the usual IID assumption is not met, and operating characteristics (coverage probability, type 1 error rate) may thus deteriorate. For...

17. Explaining Mixed Effect Machine Learning with Shapley Value

Pat Vatiwutipong (Mahidol University)

19/05/2026, 16:39

oral presentation

In many real-world datasets, observations are hierarchically structured, such as students nested within classrooms, hospitals within cities, or repeated measurements from the same patient. Performing machine learning without accounting for this clustered structure can lead to biased predictions and misleading interpretations of feature effects.

Recently, Mixed Effect Machine Learning, an...

73. ML with U-smile Visualization: A Novel Approach to Imbalanced Classification Without Thresholds

Barbara Więckowska (Department of Computer Science and Statistics, Poznan University of Medical Sciences, Poznan, Poland)

19/05/2026, 16:57

oral presentation

Background: Traditional binary classification assessment in machine learning relies heavily on decision thresholds, limiting interpretability and performance in imbalanced scenarios. While metrics like AUC under ROC (Receiver Operating Characteristic curve) provide overall performance measures, they fail to deliver class-specific insights, which is crucial for real-world applications with...