Mining Semantic Rules for Optimizing Transport Assignments in Hospitals

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.

[1]  Stefan Hastreiter,et al.  Benchmarking logistics services in German hospitals: A research status quo , 2013, 2013 10th International Conference on Service Systems and Service Management.

[2]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[3]  Johanna Völker,et al.  Statistical Schema Induction , 2011, ESWC.

[4]  W.D. Yu,et al.  Semantic web and mining in healthcare , 2006, HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services.

[5]  Rafael Berlanga Llavori,et al.  Finding association rules in semantic web data , 2012, Knowl. Based Syst..

[6]  Filip De Turck,et al.  Learning Semantic Rules for Intelligent Transport Scheduling in Hospitals , 2016, @ESWC.

[7]  Fabrice Guillet,et al.  Knowledge-Based Interactive Postmining of Association Rules Using Ontologies , 2010, IEEE Transactions on Knowledge and Data Engineering.

[8]  Angelos Charalambidis,et al.  Formulating description logic learning as an Inductive Logic Programming task , 2010, International Conference on Fuzzy Systems.

[9]  Filip De Turck,et al.  Semantic Context Consolidation and Rule Learning for Optimized Transport Assignments in Hospitals , 2016, ESWC.