Using machine-learning for differentiating anxiety vs. depression

Anxiety and depression are distinct – albeit overlapping – psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic tool, which rigorously tests for differences in cognitive biases among anxious and depressed individuals. A comprehensive behavioral test battery quantifies various cognitive biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The analysis also discloses which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.

Relevant literature:

Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021). Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. Journal of Personalized Medicine, 11(10), 957.

Richter, T., Fishbain, B., Markus, A., Richter-Levin, G., & Okon-Singer, H. (2020). Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Scientific reports, 10(1), 1-12.

Richter, T., Fishbain, B., Fruchter, E., Richter-Levin, G., & Okon-Singer, H. (2021). Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. Journal of Psychiatric Research, 141, 199-205.