We have developed a prototype computer program, MetaNet, that uses a combination of artificial neural networks and knowledge-based expert systems to assist in the diagnosis of inborn errors of metabolism in children.

Results of amino acid analysis data of normal children, and of patients diagnosed with a number of amino acid and organic acid abnormalities were used as inputs to train the neural network component of the program. To diagnose new cases, plasma or urinary amino acid results are entered. The knowledge-based expert system then asks questions of the user regarding the presence or absence of common clinical and/or biochemical abnormalities.

Using both the amino acid data and the answers to the questions, the MetaNet program integrates the output of the neural network and the results of the knowledge-based expert system to yield a provisional diagnosis.

The diagnostic output is accompanied by a numerical *belief vector*, which indicates the degree of confidence of the program in the diagnosis. Altering any of the input variables followed by reprocessing of the data generates a new diagnostic output and a revised belief vector. This allows analysis of the importance of any input variable to the proposed diagnosis. The knowledge-based expert system also includes a section entitled *Independent Metabolic Disease Reference Documents*, which provides additional information about a suspected metabolic disease when requested by the user.

The neural network component consists of eight, three-layer neural networks that are trained using a back-propagation approach. Analysis of the hidden layers following training of the neural network revealed both expected and novel, unexpected connections between specific diagnoses and clusters of amino acids. Such data may be used as a guide for future investigation of the contribution of the metabolism of specific amino acids to amino acid disorders.

The program runs under Windows 3.1 or Windows 95, and promises to be useful both as a model for computer assisted diagnosis of inborn errors and as a research tool.

J. Pepper, ServiceWare Inc., Pittsburgh, Principal Investigator

C. E. Wyatt, Applied Analytic Systems Inc., Pittsburgh, PA, MetaNet Technical Developer

D.C. Lehotay and J.T.R. Clarke, Medical Consultants, The University of Toronto Hospital for Sick Children, Ontario, Canada

Contact:

Chris E. Wyatt
Applied Analytic Systems, Inc.
600 North Bell Avenue
Carnegie, PA 15106

412.278.2360

wyatt@aasdt.com

Contact Information:

Company Name: Applied Analytic Systems, Inc.
REF URL: http://www.aasdt.com/
POSTED: Saturday, April 04, 1998