aceMachine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. We systematically searched PubMed, Embase, Web of Science, and Cochrane Libra…
Machine learning models that integrate clinical and radiomics features achieved high pooled discrimination for predicting hematoma expansion, poor functional outcome, and mortality in adults with spontaneous intracerebral hemorrhage.
Pooled performance estimates were based predominantly on internal validation, with true external validation remaining sparse, limiting confidence in generalizability.
Performance estimates were derived predominantly from internal validation sets and true external validation remains sparse, limiting generalizability to diverse cohorts.
Evidence
- Peer-reviewedBrain and Behavior2026-08-01
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Truvace Impact Record TRV-2026-0611, v1: “Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.” Truvace, 2026-08-01. /record/TRV-2026-0611 (accessed at citation time). sha256 efe84e687005e9c5…
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