ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Predicting discharge mortality after acute ischemic stroke using balanced data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning models for predicting outcomes of stroke with structured data.PloS one · 2020Pooled it
- Predicting surgical intervention in paediatric cervical abscesses using machine learning: a comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Use of artificial intelligence in the management of stroke: scoping review.Frontiers in radiology · 2025Review
- Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis.Cureus · 2023Article
- Improved performance of machine learning models in predicting length of stay, discharge disposition, and inpatient mortality after total knee arthroplasty using patient-specific variables.Arthroplasty (London, England) · 2023Article
- Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data.Frontiers in neurology · 2023Article
- Henry gas solubility optimization double machine learning classifier for neurosurgical patients.PloS one · 2023Article
- Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.Scientific reports · 2022Article
- Combined Functional Assessment for Predicting Clinical Outcomes in Stroke Patients After Post-acute Care: A Retrospective Multi-Center Cohort in Central Taiwan.Frontiers in aging neuroscience · 2022Article
- Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol.Frontiers in neurology · 2022Article
- Predicting short and long-term mortality after acute ischemic stroke using EHR.Journal of the neurological sciences · 2021Article
- Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading.Computational intelligence and neuroscience · 2021Article
- Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke.Neurocritical care · 2020Article
- Generating high-fidelity synthetic patient data for assessing machine learning healthcare software.NPJ digital medicine · 2020Article
- Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network.Diagnostics (Basel, Switzerland) · 2020Article
- Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.Journal of vascular and interventional radiology : JVIR · 2020Article
- Fighting healthcare rocketing costs with value-based medicine: the case of stroke management.BMC health services research · 2020Article
- A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.IEEE transactions on medical imaging · 2019Article
- Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.
Indexed as
Identifiers
25954451PMC4419881What 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.