On mistakes we made in prior computational psychiatry data driven approach projects and how they jeopardize translation of those findings in clinical practice

Abstract

In this work, we aimed at comparing our findings in depression detection task with similar methodologies applied in present literature. In our project we showed that when electrophysiological signal (in this case electroencephalogram, EEG) is characterized by nonlinear measures, any of seven most popular classifiers yields high accuracy on the task. Following every step done in this process we elaborated on other findings mainly from analysis of electrical signals or nonlinear analysis showing what would be optimal for further research. We focused on discussing various possible mistakes and differences that could potentially lead to unwarranted optimism and other misinterpretations of results. We also consider obstacles that this practice would be accepted for real-life application in psychiatry and some ideas how to overcome them. In Conclusion we summarize recommendation for future research in order to be easily applicable in clinical practice.