Laboratory Test Scheduling Based On Constraint Propagation

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One of the essential problems in medical decision making is the partiality of information. If knowledge is represented as rules, the number of rules needed for all the possible situations is the exponential function of the complexity of the knowledge. We propose a new model of medical knowledge representation based on constraints. The structure of a disease is represented as a set of elemental data and the constraints between them. Links between patient data and parts of the disease structure are established. While links are made, indeterminate variables in the disease structure are instantiated or constrained, and the most suitable laboratory schedule for available patient information is dynamically generated by constraint propagation.

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