ArticleScientific reports2020
Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The presymptomatic phase of amyotrophic lateral sclerosis: are we merely scratching the surface?Journal of neurology · 2021Pooled it
- The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development.Brain imaging and behavior · 2021Pooled it
- Prognostic models for amyotrophic lateral sclerosis: a systematic review.Journal of neurology · 2021Pooled it
- Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies.Brain sciences · 2026Review
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Regional Cerebral Atrophy Contributes to Personalized Survival Prediction in Amyotrophic Lateral Sclerosis: A Multicentre, Machine Learning, Deformation-Based Morphometry Study.Annals of neurology · 2025Article
- New developments in imaging in ALS.Journal of neurology · 2025Review
- The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.Brain : a journal of neurology · 2025Article
- Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.European journal of nutrition · 2024Article
- Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis.BMC medical informatics and decision making · 2024Article
- Language deficits in primary lateral sclerosis: cortical atrophy, white matter degeneration and functional disconnection between cerebral regions.Journal of neurology · 2024Article
- Accurate personalized survival prediction for amyotrophic lateral sclerosis patients.Scientific reports · 2023Article
- Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.Journal of clinical medicine · 2023Review
- Brainstem-cortex disconnection in amyotrophic lateral sclerosis: bulbar impairment, genotype associations, asymptomatic changes and biomarker opportunities.Journal of neurology · 2023Article
- Hippocampal Metabolic Alterations in Amyotrophic Lateral Sclerosis: A Magnetic Resonance Spectroscopy Study.Life (Basel, Switzerland) · 2023Article
- Algorithm-Based Risk Identification in Patients with Breast Cancer-Related Lymphedema: A Cross-Sectional Study.Cancers · 2023Article
- Machine-learning in motor neuron diseases: Prospects and pitfalls.European journal of neurology · 2022Article
- Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.iScience · 2022Article
- Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features.Journal of neurology · 2022Article
- Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.The Lancet. Digital health · 2022Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic Lateral Sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease modifying therapies. The development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for survival in ALS where result uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 5,220 patients was included as development data originating from past clinical trials, and real-world population data as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients shows an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones with relevant survival rates. These rates were defined using confidence bounds: high, intermediate, and low 1-year survival rates at respectively [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]). Predicted 1-year survival was estimated using zone membership. This approach requires a limited set of features, is easily updated, improves with additional patient data, and accounts for results uncertainty.
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