In August 2025, I had the opportunity to present my research at the Biomedical Data Science Conference 2025 held at Semmelweis University in Budapest. My presentation focused on an adaptive Bayesian learning framework integrating a Precision / Imprecision Entropy Indicator (IEI) to improve efficiency in biomedical decision-support and search processes.
The primary aim of the study was to examine how spatial patterns of informational uncertainty influence search efficiency and decision-making performance. Within a simulation-based framework, multiple search strategies were compared, including random search, random walk, genetic algorithms, and a Bayesian adaptive system. The analysis evaluated how explicitly modeling and reducing uncertainty affects the ability to locate a predefined target area.
The results demonstrated that the Bayesian adaptive search algorithm, which incorporates entropy-based uncertainty patterns, consistently achieved target detection with significantly fewer search steps compared to non-adaptive or heuristic methods. More than 300,000 posterior values were analyzed, allowing a detailed assessment of entropy field distortion and its direct relationship with search efficiency.
The presentation materials are available at the following link:
Beyond algorithmic performance, the study emphasizes a broader conceptual contribution: shifting the focus from observed outcomes to the underlying uncertainty structures that shape them. Mapping and quantifying entropy fields provides a framework for understanding why certain search or decision processes are inefficient and how targeted uncertainty reduction can improve performance. This perspective has potential relevance for biomedical decision-support systems, clinical research methodologies, and the analysis of complex healthcare processes.