ArticleAlzheimer's research & therapy2023
Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning.
Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review.Journal of the American Heart Association · 2026Pooled it
- Risk prediction models for post-stroke cognitive impairment: a systematic review and meta-analysis.Frontiers in neurology · 2026Pooled it
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Utility of Tablet-Based Eye Tracking for Early Screening of Poststroke Cognitive Impairment: Diagnostic Cohort Study.JMIR mHealth and uHealth · 2026Article
- Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.BMC medical informatics and decision making · 2026Article
- Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.Translational stroke research · 2026Article
- Psychometric Validation of a User-Centered Usability and Acceptance Instrument for Home-Based Cognitive Rehabilitation in Older Adults With Cognitive Impairment.Brain & NeuroRehabilitation · 2026Article
- Incidence and impact of brain lesions and cognitive impairment after CABG with moderate or severe cerebral artery stenosis seen on low-field MRI.Stroke and vascular neurology · 2026Article
- RNA gene expression and cognitive reserve as determinants of post-ischaemic stroke cognitive recovery.Scientific reports · 2026Article
- Integrated clinical and postmortem profiling in schizophrenia reveals a cognitive subtype linked to cerebrovascular disease.Translational psychiatry · 2026Article
- A clinical machine learning model for 1-year functional outcome prediction in acute ischemic stroke: temporal validation across evolving guidelines.Scientific reports · 2026Article
- Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning-based prediction model for cognitive frailty in elderly patients with ischaemic stroke: a prospective cohort study.Frontiers in neurology · 2026Article
- Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment.Frontiers in neuroimaging · 2026Review
- NIH Stroke Scale and age predict early post-stroke cognitive impairment.Frontiers in stroke · 2026Article
- Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.PloS one · 2026Article
- Construction of a Diagnostic Model and Drug Prediction for Postischemic Stroke Cognitive Impairment Based on Machine Learning Screening of Lactate Metabolism- and Pyroptosis-Related Genes.Human mutation · 2026Article
- Dynamic trajectories of inflammatory biomarkers and post-stroke cognitive impairment: a comprehensive review of neuroimmune mechanisms, longitudinal modeling, and clinical translation.Frontiers in neurology · 2026Review
- Analysis of risk factors for vascular dementia following thrombolytic therapy in patients with acute ischemic stroke.Medicine · 2025Observational
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesPost-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke.
methodsThis retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3-6 months, defined as an adjusted z-score of less than - 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables.
resultsA total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts.
conclusionOur findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.
Indexed as
Identifiers
What OpenQuestion holds
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.