The use of consequential reasoning in cancer chemotherapy.
Knowledge-based decision support systems traditionally rely on condition-action rule structures, an adequate representation for simple decisions. In complex domains an important part of decision-making includes analysis of the consequences of a decision. Consequential reasoning is particularly important in medicine as potential risk and/or benefit can be included. In this paper, a knowledge structure and inference engine is described that permits the representation and analysis of consequential reasoning in a computer-assisted decision support system. The use of consequential reasoning is then illustrated in an application designed to assist in cancer chemotherapy decisions. The result is a method that is sensitive to individual patient reactions to chemotherapy agents, permitting an individualized approach to therapy. Individualized drug therapy is becoming increasingly feasible due to advances made in the field of genomics. The system is structured so that new information can be incorporated easily. Although the application shown here is to chemotherapy, the general methodology can be used in any area in which the consequences should significantly influence the decision.
Hudson
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University of California, San Francisco, Fresno, California 93703, USA. hudson@ucsfresno.edu
Cohen
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ME
Hudson
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SE
D001185:Artificial Intelligence; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D004360:Drug Therapy, Computer-Assisted; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy*