ArticleJournal of personalized medicine2021
Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- 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
- Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.Translational stroke research · 2026Article
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.medRxiv : the preprint server for health sciences · 2026Article
- Development of a prediction model for hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke: a retrospective analysis.BMC medical informatics and decision making · 2025Article
- An Explainable Two-Stage Machine Learning Model for Predicting the Post-Thrombolysis Complications in Stroke Patients: A Multi-Center Study.Research (Washington, D.C.) · 2025Article
- MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke.PeerJ. Computer science · 2025Article
- Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.Frontiers in neurology · 2024Article
- A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.Biomedical engineering online · 2023Article
- Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study.Frontiers in neurology · 2023Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
- Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Hemorrhagic transformation (HT) is one of the leading causes of a poor prognostic marker after acute ischemic stroke (AIS). We compared the performances of the several machine learning (ML) algorithms to predict HT after AIS using only structured data. A total of 2028 patients with AIS, who were admitted within seven days of symptoms onset, were included in this analysis. HT was defined based on the criteria of the European Co-operative Acute Stroke Study-II trial. The whole dataset was randomly divided into a training and a test dataset with a 7:3 ratio. Binary logistic regression, support vector machine, extreme gradient boosting, and artificial neural network (ANN) algorithms were used to assess the performance of predicting the HT occurrence after AIS. Five-fold cross validation and a grid search technique were used to optimize the hyperparameters of each ML model, which had its performance measured by the area under the receiver operating characteristic (AUROC) curve. Among the included AIS patients, the mean age and number of male subjects were 69.6 years and 1183 (58.3%), respectively. HT was observed in 318 subjects (15.7%). There were no significant differences in corresponding variables between the training and test dataset. Among all the ML algorithms, the ANN algorithm showed the best performance in terms of predicting the occurrence of HT in our dataset (0.844). Feature scaling including standardization and normalization, and the resampling strategy showed no additional improvement of the ANN's performance. The ANN-based prediction of HT after AIS showed better performance than the conventional ML algorithms. Deep learning may be used to predict important outcomes for structured data-based 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.