ArticleCritical care (London, England)2025
Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.
Article in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling.Neurocritical care · 2026Article
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid Hemorrhage: The Role of CNS Infection-Related Parameters.Biomedicines · 2026Article
- Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2026Article
- Copeptin in acute ischemic stroke: a surrogate marker of AVP-system activation with temporal dynamics and clinical implications.Journal of thrombosis and thrombolysis · 2026Review
- Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.Neurosurgical review · 2026Article
- Analysis and implications of validation results for a clinical data-based early warning model for geriatric sepsis.Frontiers in public health · 2026Article
- A machine learning-based online prediction model for recurrence risk in patients withFrontiers in cellular and infection microbiology · 2026Article
- Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators.BMC medicine · 2025Article
- Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Prediction of functional outcomes in aneurysmal subarachnoid hemorrhage using pre-/postoperative noncontrast CT within 3 days of admission.NPJ digital medicine · 2025Article
- Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients.Frontiers in neurology · 2025Article
- Machine learning-based prediction of response to Ustekinumab with Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundAneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies.
methodsA multicenter study was conducted using data collected from 718 patients with aSAH who were treated at five hospitals in Japan. A deep learning model was developed to predict outcomes based on modified Rankin Scale scores using pretherapy clinical data collected from admission to the initiation of physical therapy. The model's performance was assessed using the area under the curve, and interpretability was enhanced using SHapley Additive exPlanations (SHAP). Logistic regression analysis was also performed for further validation.
resultsThe area under the receiver operating characteristic curve of the model was 0.90, with age, World Federation of Neurosurgical Societies grade, and higher brain dysfunction identified as key predictors. SHAP analysis supported the importance of these features in the prediction model, and logistic regression analysis further confirmed the model's robustness.
conclusionsThe novel deep learning model demonstrated strong predictive performance in determining functional outcomes in patients with aSAH, making it a valuable tool for guiding early rehabilitation strategies.
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