The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions.
- DOI
- 10.3389/fpsyt.2020.00472
- Published
- 2020
- Container
- Frontiers in psychiatry
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3389/fpsyt.2020.00472,
title = {The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions.},
author = {Ermers NJ and Hagoort K and Scheepers FE},
year = {2020},
journal = {Frontiers in psychiatry},
doi = {10.3389/fpsyt.2020.00472},
url = {https://doi.org/10.3389/fpsyt.2020.00472}
}RIS
TY - JOUR TI - The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions. AU - Ermers NJ AU - Hagoort K AU - Scheepers FE PY - 2020 JO - Frontiers in psychiatry DO - 10.3389/fpsyt.2020.00472 UR - https://doi.org/10.3389/fpsyt.2020.00472 ER -
APA
NJ, E., K, H., & FE, S. (2020). The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions.. Frontiers in psychiatry. https://doi.org/10.3389/fpsyt.2020.00472
Source records
- pubmed · retrieved 2026-09-25T05:25:39.148Z