P. Votruba,A. Seyfang
, M. Paesold, S. Miksch:
"Improving the Execution of Clinical Guidelines and Temporal Data Abstraction in High-Frequency Domains
Vortrag: AI Techniques in Healthcare: Evidence-based Guidelines and Protocols, Trento, Italien; 28.08.2006; in:"AI Techniques in Healthcare: Evidence-based Guidelines and Protocols
", A. ten Teije, S. Miksch, P. Lucas (Hrg.); European Coordinating Committee for Artificial Intelligence, (2006), S. 112 - 116.
[ Publication Database
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
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.