Reproducible symptom subtypes of depression identified using unsupervised machine learning
Using unsupervised machine learning on UK Biobank data, this study identifies robust and reproducible symptom subtypes of depression that align with known clinical categories and reveal novel profiles linked to specific sociodemographic factors, health conditions, and genetic risks, thereby supporting data-driven approaches for refining diagnosis and personalizing treatment.