"An important application of both data abstraction and plan execution is the execution of clinical guidelines and protocols (CGPs)
both to validate them against a large set of test cases and to provide decision support at the point of care.
CGPs can be represented and executed as a hierarchy of skeletal plans. To bridge the gap between low-level data and high-level
concepts in the CGP, intelligent temporal data abstraction must be integrated with plan execution. This project provides a
solution to this challenge... [CGPs] are translated to a high-level plan representation language which
is compiled into a network of abstraction modules by the system. This network [executes] the contents of the plans triggered
by ... patient data. By this, we seamlessly integrate the synchronisation of guideline execution with observed patient state,
complex temporal abstractions and execution of complex plans without requiring the user to handle the low-level details."
Votruba P, Seyfang A, Paesold M, Miksch S.
Environment-Driven Skeletal Plan Execution for the Medical Domain.
To appear in: Proc. AI techniques in healthcare: evidence-based guidelines and protocols, 2006.
"The execution of clinical guidelines and protocols ...
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.
...Asbru represent[s] 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 [has been] evaluated to be efficient
enough to handle high-frequency data while coping with complex
guidelines and temporal data abstraction."
Vienna University of Technology
Faculty of Informatics
Institute of Software Technology and Interactive Systems
Information Engineering Group
A-1040 Vienna, Austria