ArticleScientific reports2020
Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.Journal of neurology · 2024Pooled it
- Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer.World journal of gastroenterology · 2024Trial
- Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models.Stroke · 2023Trial
- Review
- Optimizing compressive strength prediction using adversarial learning and hybrid regularization.Scientific reports · 2024Article
- Functional Outcomes and Symptomatic Intracranial Hemorrhage After Endovascular Treatment in Acute Vertebrobasilar Artery Occlusions: External Validation of Prediction Models.Stroke (Hoboken, N.J.) · 2024Article
- Article
- Article
- Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study.International journal of general medicine · 2024Article
- Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges.The EPMA journal · 2023Review
- Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.Scientific reports · 2023Article
- Cytokine profiles of plasma extracellular vesicles as progression biomarkers in Parkinson's disease.Aging · 2023Article
- XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2023Article
- Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks.Frontiers in neurology · 2023Article
- Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management.Scientific reports · 2022Observational
- The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements.Frontiers in neurology · 2022Review
- Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.
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