Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis

Key Points

• Stroke-associated pneumonia prediction can benefit from advanced machine learning algorithms, leading to effective interventions.
• The analysis revealed that predictive models improved diagnosis accuracy by an average of 20% in the identified studies.
• Systematic review assessed data from various research articles, focusing on how machine learning can foresee pneumonia risk after strokes.
• These findings underline the potential for machine learning to enhance clinical decision-making in stroke care.