Written by Maurice M. Ohayon, MD, DSc, PhD

Reference to cite:

1) Ohayon MM. Sleep-EVAL, Knowledge Based System for the Diagnosis of Sleep and Mental Disorders. Registration #437699, Copyright Office, Canadian Intellectual Property Office. Ottawa: Industry Canada, 1994. (English, French, German, Italian, Portuguese, Spanish, Finnish, Swedish, Korean, Chinese versions).

2) Ohayon MM. Improving decision making processes with the fuzzy logic approach in the epidemiology of sleep disorders. J Psychosom Res 1999 Oct;47(4):297-311

The knowledge processor, or inference engine, is the part of the expert system that finds solutions to problems.
The inference engine uses its knowledge base to pose questions, to infer hypotheses and to deduce diagnostic conclusions.

The Neural Networks

This is a computational structure composed of three types of layers (subgroups of processing elements): input layers, output layers and hidden layers.

More simply put, a neural network is useful for solving pattern-matching problems.

In Sleep-EVAL, two neural networks are used by the inference engine to manage any uncertainty in the subject’s answers as well as in criteria and diagnoses:

      - The first neural network is a fixed one whose function is to manage fuzzy sets of answers.

The cumulative weights are used to determine the presence or absence of a criterion or a diagnosis.

In the end, each explored object (including diagnoses) will have a degree of certainty (or weight) ranging from 0.4 (completely present) to -0.4 (completely absent).

The Mathematical Preprocessor

It performs a number of mathematical operations, such as converting age into months or weeks and hours into minutes or seconds, comparing duration of symptoms or discrepancies between hours, and setting the range of responses to be entered by numerical keypad.

How does it work?

First, the inference engine starts with a general questionnaire to be asked to all participants.

Second, using the answers provided to this questionnaire, the inference engine establishes the most adequate decision trees in order to get the most probable diagnoses.

Once a potential diagnosis is found, the inference engine explores all the other avenues to have another diagnosis and may ask additional questions if it lacks the necessary elements to confirm or deny the diagnosis (differential diagnosis process).

References

Ohayon MM. Sleep-EVAL, Knowledge Based System for the Diagnosis of Sleep and Mental Disorders. Registration #437699, Copyright Office, Canadian Intellectual Property Office. Ottawa: Industry Canada, 1994. (English, French, German, Italian, Portuguese, Spanish, Finnish, Swedish, Korean, Chinese versions).
Ohayon M.  Quebec National Library, ISBN 2-921483-06-8, 1995.

Ohayon MM. Improving decision making processes with the fuzzy logic approach in the epidemiology of sleep disorders. J Psychosom Res 1999 Oct;47(4):297-311