Predicting Long-Term Depression Progression in Parkinson's Disease: A Machine-Learning Survival Analysis and Risk Score
A prediction tool using routine clinical data identifies which Parkinson's patients will develop depression, enabling earlier intervention for this common comorbidity.
Using the PPMI dataset (n=496), this study developed an explainable ML survival model with RSF (C-index 0.744) to predict depression progression in PD, stratifying patients into three clinically meaningful risk groups. The tool uses routinely collected variables (autonomic symptoms, cognition, impulse control) and could enable early personalized management of neuropsychiatric comorbidity in PD.
What the study was
- Study design
- Retrospective cohort, ML survival analysis (PPMI dataset; n=496)
- Population
- De novo, drug-naive Parkinson's disease participants (PPMI 2011-2024)
- Sample size
- 496
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- CNS Neuroscience & Therapeutics
Why it surfaced
Explainable ML model using routinely collected measures with actionable risk stratification over 10-year window; strong PPMI dataset; limited by retrospective design and single cohort validation.
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