首页|Capital Medical University Reports Findings in Machine Learning (Machine learnin g predictions of the adverse events of different treatments in patients with isc hemic left ventricular systolic dysfunction)
Capital Medical University Reports Findings in Machine Learning (Machine learnin g predictions of the adverse events of different treatments in patients with isc hemic left ventricular systolic dysfunction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Beijing, People’s Repu blic of China, by NewsRx correspondents, research stated, “This study aimed to d evelop several new machine learning models based on hibernating myocardium to pr edict the major adverse cardiac events(MACE) of ischemic left ventricular systol ic dysfunction(LVSD) patients receiving either percutaneous coronary interventio n(PCI) or optimal medical therapy(OMT). This study included 329 LVSD patients, w ho were randomly assigned to the training or validation cohort.” Our news journalists obtained a quote from the research from Capital Medical Uni versity, “Least absolute shrinkage and selection operator(LASSO) regression was used to identify variables associated with MACE. Subsequently, various machine l earning models were established. Model performance was compared using receiver o perating characteristic(ROC) curves, the Brier score(BS), and the concordance in dex(C-index). A total of 329 LVSD patients were retrospectively enrolled between January 2016 and December 2021. Utilizing LASSO regression analysis, five facto rs were selected. Based on these factors, RSF, GBM, XGBoost, Cox, and DeepSurv m odels were constructed. In the development and validation cohorts, the C-indices were 0.888 vs. 0.955 (RSF). The RSF model (0.991 vs. 0.982 vs. 0.980) had the h ighest area under the ROC curve (AUC) compared with the other models. The BS (0. 077 vs. 0.095vs. 0.077) of RSF model were less than 0.25 at 12, 18, and 24 month s.”
BeijingPeople’s Republic of ChinaAsi aAdverse Drug ReactionsCardiologyCyborgsDrugs and TherapiesEmerging Te chnologiesHealth and MedicineMachine Learning
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