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The American Board of Family Practice is developing a computer-based recertification process to generate patient simulations from a knowledge base. Simulated patients require a stochastically generated history and response to treatment, suggesting a Monte Carlo-like patient generation process. Knowledge acquisition experiments revealed that description of a patient's overall health as a node in a Monte Carlo model was difficult for domain experts to use, severely limited knowledge reusability, and created a plethora of awkwardly defined health states. We explored a model in which patients traverse several parallel health state networks simultaneously, so that overall health is a vector describing the current nodes from every Parallel Network. This model has a reasonable biological basis, more easily defined data, and greatly improved reuse potential, at the cost of more complex simulation algorithms. Experiments using osteoarthritis stages, weight classification, and absence or presence of gastric ulcers as three Parallel Networks demonstrate the feasibility of this approach to simulating patients. |
