ArticleNPJ Parkinson's disease2022
Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.
Article in NPJ Parkinson's disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it, 102 citations in OpenAlex.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- Cross-species molecular stratification enables T cell receptor diagnosis of Parkinson's disease.Genome medicine · 2026Article
- Spatiotemporal Progression Patterns of Striatal Dopamine Depletion and Cerebral Hypoperfusion in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Long-Duration Response to Levodopa in the PPMI-Cohort.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-Movement disorders clinical practice · 2026Article
- GLP-1 agonists in neurodegeneration: a multimodal biomarker-guided approach.Trends in molecular medicine · 2026Review
- Personalized metabolite biomarker predictions reveal heterogeneous characteristics of Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Genetic Associations of Parkinson's Disease Clinical, Pathological, and Data-Driven Subtypes.Genes · 2026Article
- Prognostic Value of Parkinson's Disease Subtypes in the LABS-PD Cohort: Functional Ability, Quality of Life and Mortality.Movement disorders clinical practice · 2026Article
- Subtyping Alzheimer's disease and Parkinson's disease using longitudinal electronic health records.Nature aging · 2026Article
- CDS-PD: a novel clinical decision support platform for Parkinson's disease.Scientific reports · 2026Article
- Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.NPJ Parkinson's disease · 2026Article
- CARDBiomedBench: a benchmark for evaluating the performance of large language models in biomedical research.The Lancet. Digital health · 2026Review
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
- Identification of genes associated with smell dysfunction in Parkinson's disease.Clinical parkinsonism & related disorders · 2026Article
- SPARROW: subtyping Parkinson's disease with agentic reasoning and robust omics workflow.NPJ artificial intelligence · 2026Article
- Brain age gap as predictor of disease progression in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Non-motor symptoms as critical predictors of quality of life in Parkinson's disease: a machine learning approach.Health and quality of life outcomes · 2025Observational
- MRI Epicenters Differentiate Spatiotemporal Patterns of Neurodegeneration in Parkinson's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- PDualNet: a deep learning framework for joint prediction of Parkinson's disease progression subtype and MDS-UPDRS scores.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
19 authors at 5 institutions in 2 countries.
Funding
Abstract
The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.