Simulation and Machine Learning Provide New Approaches to Examine Quality of Acute Stroke Management

Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis

“…The advances in ML have presented opportunities to harness these massive medical datasets to inform medical practice across various domains. In recent years, ML models have been widely used to solve various complex challenges in stroke, such as early stroke detection and thrombolysis decision-making [ 6 , 7 ] neuroimaging analysis [ 8 , 9 ], stroke diagnosis and severity assessment [ 10 , 11 ], candidate selection for therapeutic intervention [ 12 , 13 ], prediction of short- and long-term functional outcomes and prognosis [ [ [14] , [15] , [16] , [17] ]]. Early detection of stroke is a crucial step in ensuring effective treatment and ML has demonstrated significant value in facilitating this process [ 18 ].…”