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

TL;DR

This paper reviews past computational psychiatry projects, highlighting mistakes that hinder clinical translation, and offers best practices for improving machine learning-based depression detection methods.

Contribution

It identifies key errors in previous data-driven psychiatry studies and proposes guidelines to enhance clinical applicability of machine learning models.

Findings

Feature choice is critical for model performance.

Comparison of seven ML models on depression detection.

Summarized best practices for clinical translation.

Abstract

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.