2023
DOI: 10.1177/20552076221149528
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A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Abstract: Background Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice. Methods Our proposed algorithm is an advanced version of classical k-neare… Show more

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Cited by 5 publications
(3 citation statements)
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“…97 However, various supervised ML algorithms, using demographic, clinical, and CT imaging data, have shown a significant boost in predictive performance for sICH postthrombolysis, with AUCs ranging from 0.77 to 0.87, demonstrating adaptability across diverse ethnic groups. [98][99][100] Final infarct volume post-AIS is a critical radiographic measure linked to functional outcomes and complications like malignant cerebral edema and herniation. 101 Although imaging in the hyperacute/acute setting may not accurately reflect final infract volume, ML applications on acute MRI sequences have predicted it with high accuracy (AUC: 0.88).…”
Section: Ischemic Strokementioning
confidence: 99%
“…97 However, various supervised ML algorithms, using demographic, clinical, and CT imaging data, have shown a significant boost in predictive performance for sICH postthrombolysis, with AUCs ranging from 0.77 to 0.87, demonstrating adaptability across diverse ethnic groups. [98][99][100] Final infarct volume post-AIS is a critical radiographic measure linked to functional outcomes and complications like malignant cerebral edema and herniation. 101 Although imaging in the hyperacute/acute setting may not accurately reflect final infract volume, ML applications on acute MRI sequences have predicted it with high accuracy (AUC: 0.88).…”
Section: Ischemic Strokementioning
confidence: 99%
“…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 ].…”
Section: Introductionmentioning
confidence: 99%
“…Similar results have been achieved using extreme gradient boosting (XGBoost) and gradient boosting machines 17 . Conversely, while instance-based methods like k-Nearest-Neighbors have been employed in outcome prediction tasks 18 , 19 , they have not been applied to this specific task.…”
Section: Introductionmentioning
confidence: 99%