ArticleFrontiers in neurology2018
A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors.
Article in Frontiers in neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Bayesian networks to estimate prognosis in vascular cognitive impairment and small vessel disease: Integrated analyses of interdependent contributors to multiple outcomes.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
- A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.NPJ digital medicine · 2026Article
- Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382.BMC public health · 2025Article
- Article
- Health Management of Type 2 Diabetes Mellitus and Its Complications: A Machine Learning Algorithm-Based Retrospective Study in Chinese Communities.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Changes in facial expressions can distinguish Parkinson's disease via Bayesian inference.Frontiers in neurology · 2025Article
- Relationship between insulin resistance surrogate markers with diabetes and dyslipidemia: A Bayesian network analysis of Korean adults.PloS one · 2025Article
- Association of folate metabolism-related enzymes (MTHFR, MYD88, and TP53) and their single nucleotide polymorphisms with breast cancer susceptibility in women from Southwest China: a Bayesian network approach.Frontiers in oncology · 2025Article
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data.JMIR medical informatics · 2024Article
- A machine-learning approach to investigating the complexity of theory of mind in individuals with schizophrenia.Heliyon · 2024Article
- Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension.World journal of gastroenterology · 2024Article
- The causal link between cardiometabolic risk factors and gray matter atrophy: An exploratory study.Heliyon · 2023Article
- Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.JMIR cardio · 2023Article
- Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case.Journal of personalized medicine · 2022Article
- Influence of skin-to-skin contact on breastfeeding: results of the Mexican National Survey of Demographic Dynamics, 2018.International breastfeeding journal · 2022Article
- An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data.International journal of environmental research and public health · 2022Article
- A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study.JMIR public health and surveillance · 2022Article
- An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors.Frontiers in aging neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction with high accuracy, we constructed an inference engine for post-stroke outcomes based on Bayesian network classifiers. The prediction system that was trained on data of 3,605 patients with acute stroke forecasts the functional independence at 3 months and the mortality 1 year after stroke. Feature selection methods were applied to eliminate less relevant and redundant features from 76 risk variables. The Bayesian network classifiers were trained with a hill-climbing searching for the qualified network structure and parameters measured by maximum description length. We evaluated and optimized the proposed system to increase the area under the receiver operating characteristic curve (AUC) while ensuring acceptable sensitivity for the class-imbalanced data. The performance evaluation demonstrated that the Bayesian network with selected features by wrapper-type feature selection can predict 3-month functional independence with an AUC of 0.889 using only 19 risk variables and 1-year mortality with an AUC of 0.893 using 24 variables. The Bayesian network with 50 features filtered by information gain can predict 3-month functional independence with an AUC of 0.875 and 1-year mortality with an AUC of 0.895. We also built an online prediction service, Yonsei Stroke Outcome Inference System, to substantialize the proposed solution for patients with stroke.
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