ArticleInternational journal of environmental research and public health2019
The Use of Deep Learning to Predict Stroke Patient Mortality.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Challenges and potential of using digital biomarkers in healthcare and clinical trials.Communications medicine · 2026Review
- HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques.Scientific reports · 2025Article
- An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization.Scientific reports · 2025Article
- Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.Clinical neurology and neurosurgery · 2025Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.Scientific reports · 2024Article
- Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.Scientific reports · 2024Article
- An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.CPT: pharmacometrics & systems pharmacology · 2024Article
- Article
- A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT.PeerJ. Computer science · 2024Article
- Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches.Stroke research and treatment · 2024Article
- Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.Scientific reports · 2023Article
- The impact on clinical outcomes after 1 year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage.International journal of emergency medicine · 2023Article
- Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.Biomedicines · 2023Review
- The Mediation Effect of Perceived Attitudes toward Medical Service on the Association between Public Satisfaction with the Overall Medical Service and Self-Rated Health among the General Population in China: A Cross-Sectional Study.International journal of environmental research and public health · 2023Article
- Analyzing and predicting the risk of death in stroke patients using machine learning.Frontiers in neurology · 2023Article
- Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study.JMIR medical informatics · 2022Article
- Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke.
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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.