TY - GEN
T1 - Automatic treatment of temporal issues in clinical guidelines in the GLARE system
AU - Anselma, Luca
AU - Terenziani, Paolo
AU - Montani, Stefania
AU - Bottrighi, Alessio
PY - 2007
Y1 - 2007
N2 - Temporal constraints play a fundamental role in clinical guidelines. For example, temporal indeterminacy, constraints about duration, delays between actions and periodic repetitions of actions are essential in order to cope with clinical therapies. This paper proposes a computer-based approach in order to deal with temporal constraints in clinical guidelines. Specifically, it provides the possibility to represent such constraints and reason with them (i.e., perform inferences in the form of constraint propagation). We first propose a temporal representation formalism and two constraint propagation algorithms operating on it, and then we show how they can be exploited in order to provide clinical guideline systems with different temporal facilities. Our approach offers several advantages: for example, during the guideline acquisition phase, it enables to represent temporal constraints and to check their consistency; during the execution phase, it allows the physician to check the consistency between action execution-times and the constraints in the guidelines, and to provide queryanswering and temporal simulation facilities (e.g., when choosing among alternative paths in a guideline).
AB - Temporal constraints play a fundamental role in clinical guidelines. For example, temporal indeterminacy, constraints about duration, delays between actions and periodic repetitions of actions are essential in order to cope with clinical therapies. This paper proposes a computer-based approach in order to deal with temporal constraints in clinical guidelines. Specifically, it provides the possibility to represent such constraints and reason with them (i.e., perform inferences in the form of constraint propagation). We first propose a temporal representation formalism and two constraint propagation algorithms operating on it, and then we show how they can be exploited in order to provide clinical guideline systems with different temporal facilities. Our approach offers several advantages: for example, during the guideline acquisition phase, it enables to represent temporal constraints and to check their consistency; during the execution phase, it allows the physician to check the consistency between action execution-times and the constraints in the guidelines, and to provide queryanswering and temporal simulation facilities (e.g., when choosing among alternative paths in a guideline).
KW - artificial intelligence
KW - clinical guidelines
KW - repeated/periodic actions
KW - temporal constraint representation
KW - temporal reasoning
UR - http://www.scopus.com/inward/record.url?scp=35748985608&partnerID=8YFLogxK
M3 - Conference contribution
C2 - 17911853
AN - SCOPUS:35748985608
SN - 9781586037741
T3 - Studies in Health Technology and Informatics
SP - 935
EP - 940
BT - MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics
PB - IOS Press
T2 - 12th World Congress on Medical Informatics, MEDINFO 2007
Y2 - 20 August 2007 through 24 August 2007
ER -