Pathology

In the weightings between: the nodes or "neurons."

Bayesian belief networks are inspectable, known probabilities are required, training data are not needed, and they can classify into multiple categories.

Neural networks are not inspectable, they do not need domain expertise or known probabilities, training data are required, and they are best for a binary classification ("yes" or "no").

The Arden syntax is a standard language and format for representing the medical knowledge and algorithms required for making medical decisions. It is used in medical decision support systems.

A neural network is most appropriate, because there is no prior knowledge to allow selection of predictors, the relative weighting of predictors is unknown, a large data set of many discrete potential predictors is available, combinations of predictors may provide better discrimination than individual predictors, and the desired classification is binary (readmission likely or unlikely).

Clinical laboratory databases consist of many discrete test results that have known reference ranges and critical values. Well-established patterns of these results exist that is known as related to important clinical conditions. Writing rules that detect and alert to these patterns is straightforward.

Training data sets are given to systems initially to teach them to make correct responses. Test data sets are equivalent to the training sets but contain separate data and are used to verify the performance of the systems.