Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Qualitative process theory using linguistic variables
Qualitative process theory using linguistic variables
Maintaining knowledge about temporal intervals
Communications of the ACM
Diagnostic Problem Solving: Combining Heuristic, Approximate and Casual Reasoning
Diagnostic Problem Solving: Combining Heuristic, Approximate and Casual Reasoning
Causal understanding of patient illness in medical diagnosis
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A formal framework of knowledge to support rational psychoactive drug selection
Artificial Intelligence in Medicine
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Superficial knowledge about drug effects and interactions may provide clinicians with only a limited support for the elaboration of therapy plans. Deeper knowledge of the mechanisms through which drugs produce their effects, together with their temporal constraints, should be modelled to predict the effects and interactions of their joint administration. The present paper describes a method for modelling such deep medical knowledge, together with its causal and temporal reasoning capabilities, and compares it with classical approaches to temporal and causal reasoning, namely in the context of drug treatment applications. This method extends a previous causal functional model by allowing the representation of quantitative knowledge, the explicit representation of time intervals, and a temporal reasoning technique, Simulation by Interval Constraining. The method is illustrated by a number of examples of basic drug metabolic mechanisms; and its future use in the development of decision support systems for complex drug therapy applications is discussed.