ArticleBMC medical informatics and decision making2024
Machine learning-based predictive model for post-stroke dementia.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model.European radiology · 2026Article
- Development and validation of an explainable machine learning model integrating modified CT criteria for predicting posttraumatic acute diffuse brain swelling.La Radiologia medica · 2026Article
- Development and validation of a LASSO-logistic regression model for predicting subtherapeutic infliximab trough concentrations in patients with Crohn's disease.International journal of clinical pharmacy · 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
- An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Adverse Pregnancy Outcomes in Women With Immune Abnormalities: Machine Learning Model Development and Validation Using First-Trimester Sonographic Features.JMIR medical informatics · 2026Article
- Machine learning-based predictive model for post-stroke cardiorespiratory fitness.BMC neurology · 2026Article
- Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.PloS one · 2026Article
- Predicting social isolation in maintenance hemodialysis patients using machine learning methods: a cross-sectional study.Frontiers in psychiatry · 2026Article
- Development and external validation of a nomogram to predict the need for electrical cardioversion during radiofrequency ablation in patients with atrial fibrillation.International journal of cardiology. Heart & vasculature · 2025Article
- Article
- Social anxiety prediction model for nursing students based on machine learning: a cross-sectional survey.Frontiers in psychiatry · 2025Article
- Development and validation of a predictive nomogram for severe adverse drug reactions: a dual-center pharmacovigilance study.Frontiers in pharmacology · 2025Article
- Pre-treatment structural brain biomarkers predict response to repetitive transcranial magnetic stimulation in subjective tinnitus.Frontiers in neurologyArticle
- Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPost-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD.
methods9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron.
resultsA total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage.
conclusionOur findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.
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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.