<< Click to Display Table of Contents >> Navigation:  Using GeNIe > Support for diagnosis > Introduction to GeNIe Diagnosis

Diagnosis is one of the most successful applications of Bayesian networks. The ability of probabilistic knowledge representation techniques to perform a mixture of both predictive and diagnostic inference makes it very suitable for diagnosis. Bayesian networks can perform fusion of observations such as patient (or equipment) history and risk factors with symptoms and test results. This section reviews special features of GeNIe that support diagnostic applications.

A diagnostic model built using GeNIe represents various components of a system, possible faulty behaviors produced by the system (symptoms), along with results of possible diagnostic tests. The model essentially captures how possible defects of the system (whether it is a natural system, such as human body, or a human-made device, such as a car, an airplane, or a copier) can manifest themselves by error messages, symptoms, and test results. Using such a model, GeNIe produces a ranked list of the most likely defects and a ranked list of the most informative and cost-effective tests. The following sections assume that you are already familiar with using the plain version of GeNIe.

In section Enabling diagnostic extensions, we will learn how to enable the diagnostic features of GeNIe and how to define individual nodes of a model and their properties for the purpose of diagnosis.

Section Spreadsheet View discusses a special extension of GeNIe that is useful in rapid model building - all properties of every variable are listed in one window and the user specifying a model can move rapidly between variables and enter their specifications into the model.

Section Diagnosis window describes a special dialog window that allows the user to use a diagnostic model on real cases. The window allows for observing symptoms and signs, entering test results, and seeing GeNIe's suggestions as to what tests to perform next and what the probabilities of various faults are.

Section Diagnostic case management discusses how diagnostic cases can be saved to and retrieved from permanent storage (disk files).

Finally, section Cost of observation covers encoding and using costs of diagnosis as part of the model.