ArticleJournal of neurology2022
Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Development of a Novel Machine Learning Method for Estimation of Life-Long Chronic Disease Progression and Its Application to Type 2 Diabetes.Clinical and translational science · 2025Trial
- Prevalence and impact of comorbidities in amyotrophic lateral sclerosis.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- An efficient dimensionality reduction framework using metaheuristic optimization with deep learning models for amyotrophic lateral sclerosis disease progression prediction.Scientific reports · 2025Article
- Functional Impairment in Behavioral Variant Frontotemporal Dementia: Cognitive, Behavioral, Personality, and Brain Perfusion Contributions.Journal of personalized medicine · 2025Article
- Prognostic factors affecting ALS progression through disease tollgates.Journal of neurology · 2024Article
- Predictive modeling of ALS progression: an XGBoost approach using clinical features.BioData mining · 2024Article
- Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study.BMC medical informatics and decision making · 2024Article
- Update on recent advances in amyotrophic lateral sclerosis.Journal of neurology · 2024Review
- A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population.Scientific reports · 2024Article
- Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics.Biomedicines · 2023Article
- Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.Journal of clinical medicine · 2023Review
- Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.Annals of clinical and translational neurology · 2023Article
- Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis.Scientific reports · 2023Article
- Deep learning methods to predict amyotrophic lateral sclerosis disease progression.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
objectiveTo employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival.
methodsWe employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points.
resultsDBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively).
conclusionsProvided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.
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