Proposed methodology for knowledge acquisition: a study on congenital heart disease diagnosis.
This paper proposes a methodology for knowledge acquisition (KA) from multiple experts, in an attempt to elicit the heuristic rules followed by the physician in diagnosing twelve frequently occurring congenital heart diseases (CHD). Twenty-two pediatric cardiologists and twenty-three general cardiologists were interviewed with this technique; 274 interviews were conducted, 169 with the 22 experts, 105 with the 23 non-experts. A graph formalism was employed to represent their reasoning model, leading to the construction of a "mean reasoning model" for each diagnosis, separately for experts and non-experts. The results indicate that experts, compared to non-experts, tend to build knowledge representation models (KRM) that are smaller and less complex. Qualitative differences in information utilization between the two groups were also observed. Entropy analysis suggests a greater objectivity and cohesion of the experts' model.
Leão
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BF
Instituto de Cardiologia do Rio Grande do Sul, Porto Alegre, Brazil.
Rocha
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AF
D000465:Algorithms; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006330:Heart Defects, Congenital / Q000175:diagnosis*; D006801:Humans