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Knowledge-Based Assistant for Electromyography

Ziel

The aim of the EMG project was to develop a knowledge-based assistant to support physicians in all stages of an electromyographical (EMG) examination of patients with neurological diseases.
The objective was to produce a system sufficiently robust to withstand clinical trials in a neurophysiological laboratory. Particular attention was given to involving users in the definition of requirements and in system acceptance testing, and to bringing medical knowledge-based systems to a fully functional state.
The aim of the project was to develop a knowledge based assistant to support physicians in all stages of an electromyographical (EMG) examination of patients with neurological diseases. The objective was to produce a system sufficiently robust to withstand clinical trials in a neurophysiological laboratory. Particular attention was given to involving users in the definition of requirements and in system acceptance testing, and to bringing medical knowledge based systems to a fully functional state. The prototype EMG expert system supports the diagnostician in the analysis of EMG signals and advises on the test procedures to be performed. It includes a report generator, and contains a database of case studies. It incorporates a casual probabilistic network model to allow a unified approach to planning, diagnosis, explanation and reporting. The following major features were developed subsequently: robust inference systems; new ways of handling uncertainty by probabilistic methods; and methodologies of general applicability in knowledge representation, blackboard architecture, and user interface specification.
The prototype EMG expert system constructed in Phase I supports the diagnostician in the analysis of EMG signals and advises on the test procedures to be performed. It includes a report generator, and contains a database of case studies. It incorporates acausal-probabilistic network model to allow a unified approach to planning, diagnosis, explanation and reporting.
Phase II saw a substantial improvement in real-time performance and the development of the following major features:
-robust inference systems
-new ways of handling uncertainty by probabilistic methods
-methodologies of general applicability in knowledge representation, blackboard architecture, and user-interface specification.
Exploitation
The integrated EMG knowledge-based assistant will broaden the scope of the use of electrophysiological techniques.
An expert system shell based on causal-probabilistic reasoning, HUGIN, has been developed and is now available on the market.

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AXION A/S
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BRENGNOEDVEJ 144
3460 BIRKEROED
Dänemark

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