ArticleDiagnostics (Basel, Switzerland)2021
The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- A novel predictive model for evaluating thrombus regression in postpartum venous thromboembolism patients.International journal of clinical pharmacy · 2026Article
- Associations of blood cell indices with the risk of ankylosing spondylitis: A retrospective case-control and machine learning modeling study.SAGE open medicine · 2026Article
- Machine learning-based study of the predictors of clinically important change in patient-reported outcomes in bilateral upper-limb function in patients receiving robotic stroke rehabilitation.Frontiers in rehabilitation sciences · 2026Article
- Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning.Scientific reports · 2024Article
- Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach.Neurorehabilitation and neural repair · 2024Article
- Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework.European radiology experimental · 2024Article
- Impact of Interprofessional Collaborative Practice on Functional Improvements Among Post-Acute Stroke Survivors: A Retrospective Cross-Sectional Study.Journal of multidisciplinary healthcare · 2024Article
- Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study.International journal of environmental research and public health · 2023Article
- Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study.Journal of neuroengineering and rehabilitation · 2023Article
- The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.Journal of neuroengineering and rehabilitation · 2023Article
- Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.Frontiers in public health · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors' ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.
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