首页|College of Science Reports Findings in Ischemia (Development and internal valida tion of machine learning-based models and external validation of existing risk s cores for outcome prediction in patients with ischaemic stroke)
College of Science Reports Findings in Ischemia (Development and internal valida tion of machine learning-based models and external validation of existing risk s cores for outcome prediction in patients with ischaemic stroke)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Vascular Diseases and Conditions - Ischemia is the subject of a report. According to news reporting fr om Murdoch, Australia, by NewsRx journalists, research stated, "We developed new machine learning (ML) models and externally validated existing statistical mode ls [ischaemic stroke predictive risk score (iScore) and total led health risks in vascular events (THRIVE) scores] for pred icting the composite of recurrent stroke or all-cause mortality at 90 days and a t 3 years after hospitalization for first acute ischaemic stroke (AIS). In adult s hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest ( RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composi te outcomes after AIS hospitalization, using data from 721 patients and 90 poten tial predictor variables." The news correspondents obtained a quote from the research from the College of S cience, "At 90 days and 3 years, 11 and 34% of patients, respectiv ely, reached the composite outcome. For the 90-day prediction, the area under th e receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year predictio n, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for l ong-term outcome prediction."
MurdochAustraliaAustralia and New Ze alandAngiologyCardiologyCyborgsEmerging TechnologiesHealth and Medicin eIschemiaMachine LearningRisk and PreventionSupport Vector MachinesVas cular Diseases and Conditions
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