ArticleFrontiers in neurology2023
Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Risk prediction models for discharge disposition in patients with stroke: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Machine learning versus conventional methods for prehospital detection of stroke due to large vessel occlusion or intracranial haemorrhage.European stroke journal · 2026Article
- Symptom Network and Lesion Mapping of Post-Stroke Depression, Cognitive Impairment, and Dysfunction: A Multicenter Cohort Study.Brain and behavior · 2026Observational
- Predicting post-stroke functional outcome using explainable machine learning and integrated data.Scientific reports · 2026Article
- Construction and multi-center validation of a model based on blending ensemble learning for predicting stroke-associated pneumonia following acute ischemic stroke.Frontiers in neurology · 2026Article
- Establishment of a machine learning prediction model for Wallerian degeneration after ischemic stroke.Frontiers in medicine · 2026Article
- Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications.Cureus · 2025Review
- Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images.Journal of imaging informatics in medicine · 2025Article
- Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.Bioengineering (Basel, Switzerland) · 2025Article
- Reliability and validity of the Brief Attention and Mood Scale of 7 Items: a self-administered, online assessment.Frontiers in psychology · 2025Article
- Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods.Therapeutic advances in neurological disorders · 2025Article
- Advancing ischemic stroke diagnosis and clinical outcome prediction using improved ensemble techniques in DSC-PWI radiomics.Scientific reports · 2024Article
- Review
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Differentiation of speech in Parkinson's disease and spinocerebellar degeneration using deep neural networks.Journal of neurology · 2024Article
- Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.PloS one · 2024Article
- Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.Sensors (Basel, Switzerland) · 2023Article
- Explainable machine learning reveals multifactorial drivers of early intracranial hematoma progression in traumatic brain injury: development of a SHAP-guided SVM nomogram.Frontiers in neurologyArticle
- Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors. Methods: We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations. Results: The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression. Conclusion: Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.
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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.