@conference{281,
  keywords = {clinical guidelines, Temporal Data Abstaraction, High-Frequency Domains},
  author = {Peter Votruba and Andreas Seyfang and Michael Paesold and Silvia Miksch},
  title = {{Improving the Execution of Clinical Guidelines and Temporal Data Abstraction in High-Frequency Domains}},
  abstract = {Abstract. The execution of clinical guidelines and protocols (CGP) is a challenging task in high-frequency domains such as Intensive Care Units. On the one hand, sophisticated temporal data abstraction is required to match the low-level information from monitoring devices and electronic patient records with the high-level concepts in the CGP. On the other hand, the frequency of the data delivered by monitoring devices mandates a highly efficient implementation of the reasoning engine which handles both data abstraction and execution of the guideline. The language Asbru represented CGPs as a hierarchy of skeletal plans and integrates intelligent temporal data abstraction with plan execution to bridge the gap between measurements and concepts in CGPs. In this paper, we present our Asbru interpreter, which complies abstraction rules and plans into a network of abstraction modules by the system. This network performs the content of the plans triggered by the arriving patient data. Our approach evaluated to be efficient enough to handle high-frequency data while coping with complex guidelines and temporal data abstraction.},
  year = {2006},
  journal = {Workshop on AI Techniques in Healthcare: Evidence-based Guidelines and Protocols held in conjunction with European Conference on Artificial Intelligence (ECAI-2006)},
  pages = {112–116},
  url = {http://www.cvast.tuwien.ac.at/sites/default/files/publications/PDF/2006/ecai_ws_cgp_2006/votruba_asbru_interpreter/final/votruba_2006_ecai_ws_cgb_asbru_interpreter.pdf},
  language = {eng},
}