ArticleScientific reports2020
Machine learning to predict mortality after rehabilitation among patients with severe stroke.
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 36 papers, 4 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Assessment of multidimensional functional recovery post-stroke: a systematic review of outcome measures and their psychometric properties.BMC neurology · 2026Pooled it
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Machine learning-based prediction model for long-term mortality after ischemic stroke.Scientific reports · 2026Article
- Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?Journal of neuroengineering and rehabilitation · 2026Article
- Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- A review of machine learning applications in heart health.Biomedical engineering online · 2025Review
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Machine learning applications in stroke medicine: advancements, challenges, and future prospectives.Neural regeneration research · 2024Review
- Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
- Towards Interpretable Multimodal Predictive Models for Early Mortality Prediction of Hemorrhagic Stroke Patients.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model.Frontiers in physiology · 2023Article
- Clustering and prediction of long-term functional recovery patterns in first-time stroke patients.Frontiers in neurology · 2023Article
- Rehabilomics: A state-of-the-art review of framework, application, and future considerations.Frontiers in neurology · 2023Review
- A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks.Diagnostics (Basel, Switzerland) · 2022Article
- A machine learning approach for predicting suicidal ideation in post stroke patients.Scientific reports · 2022Article
- Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.Biomedicines · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.
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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.