Deep learning methods hold strong promise for identifying biomarkers for
clinical application. However, current approaches for psychiatric
classification or prediction do not allow direct interpretation of
original features. In the present study, we introduce a sparse deep
neural network (DNN) approach to identify sparse and interpretable
features for schizophrenia (SZ) case–control classification. An L0-norm
regularization is implemented on the input layer of the network for
sparse feature selection, which can later be interpreted based on
importance weights.
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