Mining semantic rules for optimizing transport assignments in hospitals

Abstract Healthcare is under high financial pressure and hospitals struggle to balance budgets while maintaining quality. In the AORTA project a semantic platform is being developed to optimize transport task scheduling and execution in hospitals by providing a dynamic scheduler with an up-to-date view about the current context gathered by smart devices. This paper details the self-learning module that combines semantic web technologies with association rule mining to learn the causes of late transports