On mistakes we made in prior Computational Psychiatry Data driven approach projects and how they jeopardize translation of those findings in clinical practice
This work addresses the challenge of translating computational psychiatry findings into clinical practice, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper analyzed seven machine learning models for depression detection, highlighting that feature choice is critical for performance, and summarized optimal practices to improve accuracy and clinical translation.
After performing comparison of the performance of seven different machine learning models on detection depression tasks to show that the choice of features is essential, we compare our methods and results with the published work of other researchers. In the end we summarize optimal practices in order that this useful classification solution can be translated to clinical practice with high accuracy and better acceptance.