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Knowledge-based Bias Correction - A Case Study in Veterinary Decision Support

In collaboration with experts from veterinary research institutes throughout Europe, we developed a decision-support system for the early detection of Classical Swine Fever in pigs. For evaluating our system's diagnostic performance, practitioners and researchers collected data from the real-world field and from laboratory experiments. Originating from different sources, these data could not be viewed as constituting an unbiased sample from a single probability distribution. In this paper, we present a knowledge-based method for correcting the biases in estimates from such divergent data. We demonstrate the use of our method for estimating the sensitivity and specificity characteristics of our veterinary decision-support system.