ArticleScientific reports2022
Deep learning methods to predict amyotrophic lateral sclerosis disease progression.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 39 citations in OpenAlex.
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- An efficient dimensionality reduction framework using metaheuristic optimization with deep learning models for amyotrophic lateral sclerosis disease progression prediction.Scientific reports · 2025Article
- Distinguishing amyotrophic lateral sclerosis from radiculopathy using machine learning to analyze nerve conduction data.Scientific reports · 2025Article
- Nutritional Interventions in Amyotrophic Lateral Sclerosis: From Ketogenic Diet and Neuroprotective Nutrients to the Microbiota-Gut-Brain Axis Regulation.Molecular neurobiology · 2025Review
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- Predictive modeling of ALS progression: an XGBoost approach using clinical features.BioData mining · 2024Article
- Review
- The Role of Ferroptosis in Amyotrophic Lateral Sclerosis Treatment.Neurochemical research · 2024Review
- Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank.Journal of neurology · 2024Article
- Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank.medRxiv : the preprint server for health sciences · 2024Article
- Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.Annals of clinical and translational neurology · 2023Article
- Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.Archives of pharmacal research · 2023Review
- Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.Life (Basel, Switzerland) · 2023Article
- Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis.Scientific reports · 2023Article
- Review
- Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- ALSGate: An Efficient Gated Mixture-of-Experts Model for Reliable ALS Detection Using EMG Signals.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient's treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.
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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.