In medical classification tasks, it is important to gain information about how decisions are made to ground and reflect therapies based on this knowledge. Neural black box mechanisms are not suitable for such tasks, whereas symbolic methods which extract explicit rules are, though their tolerance with respect to noise is often smaller since they do not rely on distributed representations. In this article, we test three hybrid prototype-based neural models which combine neural representations with explicit information representation in comparison to classical decision trees for mental disease classification. Depending on the model, information about relevant input attributes and explicit rules can be derived.
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