ArticleBrain sciences2021
Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Analysis of recovery-related white matter microstructures in visual spatial neglect based on diffusion tensor imaging.Brain imaging and behavior · 2026Article
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications.Oxford open neuroscience · 2026Article
- Factors Influencing Virtual Art Therapy in Patients with Stroke.Brain sciences · 2025Article
- How Do the Timing of Early Rehabilitation Together with Cognitive and Functional Variables Influence Stroke Recovery? Results from the CogniReMo Italian Multicentric Study.Healthcare (Basel, Switzerland) · 2025Article
- Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.Stroke · 2025Article
- Frontiers in neuro-rehabilitation: neural basis of cognitive contributions to movement across neurological populations.Frontiers in neurology · 2025Review
- Predicting individual long-term prognosis of spatial neglect based on acute stroke patient data.Brain communications · 2025Article
- Secure attachment to caregiver prevents adult depressive symptoms in a sex-dependent manner: A translational study.iScience · 2024Article
- The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study.Journal of clinical medicine · 2024Article
- Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.Biomedicines · 2024Review
- Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach.Journal of functional morphology and kinesiology · 2024Article
- Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis.Journal of clinical medicine · 2024Article
- Is the Combination of Robot-Assisted Therapy and Transcranial Direct Current Stimulation Useful for Upper Limb Motor Recovery? A Systematic Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2024Review
- The Future of Neurorehabilitation: Putting the Brain and Body Together Again.Brain sciences · 2023Article
- The impact of cognitive function deficits and their recovery on functional outcome in subjects affected by ischemic subacute stroke: results from the Italian multicenter longitudinal study CogniReMo.European journal of physical and rehabilitation medicine · 2023Observational
- Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis.Biomolecules · 2023Article
- The aging mind: A complex challenge for research and practice.Aging brain · 2023Review
- A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.Sensors (Basel, Switzerland) · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.
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