Evaluating the impact of machine learning models on adult major depressive disorder using conventional treatment strategies: a systematic review approach
Abstract Background Major Depressive Disorder (MDD) is a leading cause of global disability often treated through a trial-and-error approach, yet treatment response to antidepressants remains highly variable, with remission rates below 50% after initial treatment. Predicting treatment outcomes throu...
Saved in:
| Main Authors: | Nishant Yadav, Anamika Gulati, Varun Gulati, Prashant Yadav |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12982-025-00816-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder
by: Caroline W. Grant, et al.
Published: (2025-06-01) -
Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods
by: Qi Sang, et al.
Published: (2025-04-01) -
Mechanistic intersections between migraine and major depressive disorder
by: Micah Johnson, et al.
Published: (2025-07-01) -
Altered brain regional homogeneity, depressive symptoms, and cognitive impairments in medication-free female patients with current depressive episodes in bipolar disorder and major depressive disorder
by: Sulin Ni, et al.
Published: (2024-12-01) -
Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity
by: Yibo Jiang, et al.
Published: (2025-07-01)