Artificial Neural Networks Can Distinguish Novice and
Expert Strategies During Complex Problem-Solving
Ronald H. Stevens, Ph.D., Alina C. Lopo, MD,
PhD, Peter Wang, M.S.
To determine whether expert problem-solving strategies can be identified
within a large number of student performances of complex medical diagnostic
Self-organizing artificial neural networks were trained to categorize the
performances of infectious disease subspecialists on six computer-based
clinical diagnostic simulation that used the sequence of diagnostic tests
requested as the input data. Six hundred seventy-six student solutions to
these problems were presented to these trained neural networks to determine
which, if any, of the student solutions represented those of the experts.
For each simulation, the expert performances clustered around one dominant
output neurode, indicating that there were common problem-specific features
associated with the experts' problem-solving performances. When the
performances of students who also made correct problem diagnoses were tested
on these expert-trained neural networks, 17% were classified as representing
expert strategies, indicating that expert performance was a somewhat rare and
inconsistent occurrence among the students.
The ability to identify a small number of expert-like strategies within a
large body of student performances may provide an opportunity to study the
dynamics of complex learning at both individual and population levels as well
as the emergence of medical diagnostic expertise.
Journal of the American
Medical Informatics Association, 1996 Mar-Apr, 3:131-8.