I am pleased to participate in the Biomedical Data Science Conference 2026, held at Semmelweis University in Budapest, Hungary, from July 15–17, 2026.
At the conference, I will present my research entitled “Uncertainty-Aware Path Selection in Graphs Using an Adapted Imprecision Entropy Indicator.” The work explores how uncertainty can be incorporated into graph-based decision and path-selection problems by combining entropy-based measures with Bayesian learning.
The study focuses on an important limitation of traditional graph-based approaches: paths with the same length or distance may differ substantially in the uncertainty associated with the decisions required to follow them. The proposed approach uses an adapted Imprecision Entropy Indicator (IEI) to quantify uncertainty at both node and path levels and to support the identification of lower-uncertainty routes.
This proof-of-concept study represents another step in my broader research on entropy-based uncertainty measurement, biostatistics, and biomedical data science, with the longer-term aim of exploring potential applications in more complex clinical and healthcare decision-making environments.
I am grateful to my co-authors and colleagues for their contribution and support, and I look forward to discussing the work with researchers from the biomedical data science community during the conference.
Biomedical Data Science Conference 2026
📍 Semmelweis University, Budapest, Hungary
📅 July 15–17, 2026
Presentation: Uncertainty-Aware Path Selection in Graphs Using an Adapted Imprecision Entropy Indicator
