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After performing comparison of the performance of seven different machinelearning models on detection depression tasks to show that the choice offeatures is essential, we compare our methods and results with the publishedwork of other researchers. In the end we summarize optimal practices in orderthat this useful classification solution can be translated to clinical practicewith high accuracy and better acceptance.

On mistakes we made in prior Computational Psychiatry Data driven  approach projects and how they jeopardize translation of those findings in  clinical practice