ArticleScientific reports2019
Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for amyotrophic lateral sclerosis: a systematic review.Journal of neurology · 2021Pooled it
- Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles.Scientific reports · 2023Trial
- Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.Scientific reports · 2021Trial
- Three-way multi-criteria decision-making with linguistic information using prospect-regret theory and generalised TODIM for heart failure data analysis.Scientific reports · 2026Article
- Comparison of Demographics: National Amyotrophic Lateral Sclerosis Registry and Clinical Trials Data.Journal of racial and ethnic health disparities · 2025Article
- Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.Nature communications · 2024Article
- Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis.BMC medical informatics and decision making · 2024Article
- Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.Radiology. Artificial intelligence · 2024Article
- Ensemble machine learning reveals key features for diabetes duration from electronic health records.PeerJ. Computer science · 2024Article
- Elevated peripheral levels of receptor-interacting protein kinase 1 (RIPK1) and IL-8 as biomarkers of human amyotrophic lateral sclerosis.Signal transduction and targeted therapy · 2023Article
- Combining Multiple Multimodal Speech Features into an Interpretable Index Score for Capturing Disease Progression in Amyotrophic Lateral Sclerosis.Interspeech · 2023Article
- Review
- Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.BMC medical informatics and decision making · 2023Article
- Deep learning methods to predict amyotrophic lateral sclerosis disease progression.Scientific reports · 2022Article
- Nine quick tips for pathway enrichment analysis.PLoS computational biology · 2022Article
- Ten simple rules for organizing a special session at a scientific conference.PLoS computational biology · 2022Article
- Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.Journal of neurology · 2022Article
- Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.The Lancet. Digital health · 2022Article
- Article
- The ABC recommendations for validation of supervised machine learning results in biomedical sciences.Frontiers in big data · 2022Article
Corrections and comments
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.
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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.