A causal and temporal reasoning model and its use in drug therapy applications

摘要

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.