首页|St Vincent's University Hospital Reports Findings in Thrombectomy (iSPAN: Explai nable prediction of outcomes post thrombectomy with Machine Learning)
St Vincent's University Hospital Reports Findings in Thrombectomy (iSPAN: Explai nable prediction of outcomes post thrombectomy with Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery - Thrombectomy is the subject of a report. According to news reporting from Dublin, Ireland, b y NewsRx journalists, research stated, "This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy. This retrospective study i ncluded all patients aged over 18 years with an anterior circulation stroke trea ted at a thrombectomy centre from 2010 to 2020 with external validation." The news correspondents obtained a quote from the research from St Vincent's Uni versity Hospital, "The primary outcome was day 90 mRS 3. Existing clinical score s (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinica l score (iSPAN) was derived by adding an optimised weighting of the most importa nt ML features to the SPAN. 812 patients were initially included (397 female, av erage age 73), 63 for external validation. The best performing clinical score an d ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967 , 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was foun d overall and our XGB model was more accurate than SPAN (p <0.0018). The most important features were Age, mTICI and total number of passes . The addition of 11 points for mTICI of 2B and 3 points for 3 passes to the SPA N achieved the best accuracy and was used to create the iSPAN. iSPAN was not sig nificantly less accurate than our XGB model (p > 0.5). I n the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively. iSP AN incorporates machine-derived features to achieve better predictions compared to existing clinical scores."
DublinIrelandEuropeCyborgsEmergi ng TechnologiesHealth and MedicineMachine LearningSurgeryThrombectomy
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