ArticleJournal of research in medical sciences : the official journal of Isfahan University of Medical Sciences2020
The outcome in patients with brain stroke: A deep learning neural network modeling.
Article in Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 5 citations in OpenAlex.
- Machine Learning Predictive Models for Survival in Patients with Brain Stroke.Health promotion perspectives · 2025Article
- Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.Heliyon · 2024Article
- Functional improvement for patients with stroke receiving postacute care rehabilitation program.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Article
- Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.Scientific reports · 2023Article
- Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.Joint diseases and related surgery · 2022Article
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe artificial intelligence field is obtaining ever-increasing interests for enhancing the accuracy of diagnosis and the quality of patient care. Deep learning neural network (DLNN) approach was considered in patients with brain stroke (BS) to predict and classify the outcome by the risk factors. MATERIALS AND
methodsA total of 332 patients with BS (mean age: 77.4 [standard deviation: 10.4] years, 50.6% - male) from Imam Khomeini Hospital, Ardabil, Iran, during 2008-2018 participated in this prospective study. Data were gathered from the available documents of the BS registry. Furthermore, the diagnosis of BS was considered based on computerized tomography scans and magnetic resonance imaging. The DLNN strategy was applied to predict the effects of the main risk factors on mortality. The quality of the model was measured by diagnostic indices.
resultsThe finding of this study for 81 selected models demonstrated that ranges of accuracy, sensitivity, and specificity are 90.5%-99.7%, 83.8%-100%, and 89.8%-99.5%, respectively. Based on the optimal model (tangent hyperbolic activation function with the minimum-maximum hidden units of 10-20, max epochs of 400, momentum of 0.5, and learning rate of 0.1), the most important predictors for BS mortality were time interval after 10 years (accuracy = 92.2%), age category (75.6%), the history of hyperlipoproteinemia (66.9%), and education level (66.9%). The other independent variables are at moderate importance (66.6%) which include sex, employment status, residential place, smoking habits, history of heart disease, cerebrovascular accident type, blood pressure, diabetes, oral contraceptive pill use, and physical activity.
conclusionThe best means for dropping the BS load is effective BS prevention. DLNN strategy showed a surprising presentation in the prediction of BS mortality based on the main risk factors with an excellent diagnostic accuracy. Moreover, the time interval after 10 years, age, the history of hyperlipoproteinemia, and education level are the most important predictors for BS.
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