Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review
Clinical Snapshot
PICO Framework
| P — Population | Studies examining machine learning applications in stroke care across prevention levels |
| I — Intervention | Machine learning models and algorithms applied to stroke research |
| C — Comparator | Not applicable (scoping review methodology) |
| O — Outcomes | Machine learning model performance, clinical applications, and implementation across primary, secondary, and tertiary stroke prevention |
Bottom Line
This scoping review maps the landscape of machine learning applications in stroke care across the Earlier Medicine framework, revealing a research focus heavily weighted toward tertiary prevention (chronic care) rather than primary prevention or acute detection. While 105 studies demonstrate growing interest in ML for stroke care, significant gaps exist in early risk prediction and acute-phase applications. The reliance on routine clinical variables (age, NIHSS, glucose, BMI) suggests practical implementation potential, but challenges in data standardisation, model interpretability, and external validation limit immediate clinical translation. The dominance of traditional ML models over deep learning approaches may reflect the need for transparency in clinical decision-making. For Australian clinicians, this review highlights opportunities to develop ML tools for stroke prevention and early detection, though robust validation studies in local populations would be essential before implementation.
Key Findings
Effect Size: Not applicable (scoping review)
Primary Outcome: Distribution of ML applications across stroke prevention levels with tertiary prevention being most studied
Nnt Or Sensitivity: Promising predictive accuracy reported but specific performance metrics not quantified in abstract
Clinical Application
Implementation challenges identified including data heterogeneity, model transparency, and external validation requirements Relevant to Australian stroke care pathways; could inform National Stroke Foundation guidelines and integration with existing stroke units, though validation in Australian populations would be required Stroke patients across all care levels, though most evidence relates to tertiary prevention and chronic care
Abstract
This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proactive and personalized care through primary (preventive care), secondary (acute care), and tertiary (chronic care) prevention levels. Following PRISMA-ScR guidelines, studies were identified from PubMed and Scopus, yielding 105 eligible studies. Most studies focused on tertiary prevention, such as predicting in-hospital mortality, complications, and functional outcomes, while fewer addressed early risk prediction or acute-phase detection. Common predictors included age, NIHSS score, glucose, stroke volume, BMI, hypertension, and diabetes, reflecting reliance on routine clinical data. Traditional ML models (e.g., logistic regression, random forest, SVM) remain dominant, although Ensemble/Hybrid and Deep Learning models have increased steadily since 2021. ML demonstrated promising predictive accuracy and clinical potential, though challenges remain in data heterogeneity, model transparency, and external validation.
References
- 1.Putri, I. A. A., Rahmanti, A. R., Sanjaya, G. Y., Wulandari, H., Lazuardi, L., & Nguyen, H. X. (2026). Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review. Studies in Health Technology and Informatics. https://doi.org/10.3233/SHTI260177
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