ArticleDiagnostics (Basel, Switzerland)2021
Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.Frontiers in computational neuroscience · 2026Article
- Toward Precision Post-Stroke Rehabilitation Medicine: Integrating Molecular, Imaging, and Computational Biomarkers for Functional Outcome Prediction.Journal of clinical medicine · 2025Review
- Assessment of the Negative Impact of Urban Air Pollution on Population Health Using Machine Learning Method.International journal of environmental research and public health · 2023Article
- Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.Biomedicines · 2022Review
- A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke.Journal of clinical medicine · 2022Article
- Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors.Clinical and experimental otorhinolaryngology · 2022Article
- Clinical evaluation of acute necrotizing encephalopathy in children.Frontiers in pediatrics · 2022Article
- Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses.Brain sciences · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the feasibility of brainstem auditory evoked potential (BAEP) for rehabilitation prognosis prediction in patients with ischemic stroke, 181 patients were tested using the Korean version of the modified Barthel index (K-MBI) at admission (basal K-MBI) and discharge (follow-up K-MBI). The BAEP measurements were performed within two weeks of admission on average. The criterion between favorable and unfavorable outcomes was defined as a K-MBI score of 75 at discharge, which was the boundary between moderate and mild dependence in daily living activities. The changes in the K-MBI scores (discharge-admission) were analyzed by nonlinear regression models, including the artificial neural network (ANN) and support vector machine (SVM), with the basal K-MBI score, age, and interpeak latencies (IPLs) of the BAEP (waves I, I-III, and III-V). When including the BAEP features, the correlations of the ANN and SVM regression models increased to 0.70 and 0.64, respectively. In the outcome prediction, the ANN model with the basal K-MBI score, age, and BAEP IPLs exhibited a sensitivity of 92% and specificity of 90%. Our results suggest that the BAEP IPLs used with the basal K-MBI score and age can play an adjunctive role in the prediction of patient rehabilitation prognoses.
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