摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。本研究旨在建立几种新的基于冬眠心肌的机器学习模型，以预测接受经皮冠状动脉介入治疗（PCI）或最佳药物治疗（OMT）的缺血性左心室收缩功能障碍（LVSD）患者的主要心脏不良事件（MACE），包括329例LVSD患者。W HO被随机分配到培训或验证队列。我们的新闻记者从首都医科大学的研究中获得了一句话:“使用最小绝对收缩和选择算子(LASSO)回归来识别与MACE相关的变量,随后建立了各种机器L收益模型,并使用ROC曲线(Receiver O perating characterity,ROC)曲线、Brier评分(Brier score,BS)、MACE和MA回顾性分析了2016年1月至2021年12月329例LVSD患者,采用LASO回归分析方法,选取5个事实RS,根据这些因素构建RSF、GBM、XGBoost、Cox和DeepSurv模型。C指数为0.888 vs 0.955(RSF)。与其他模型相比，RSF模型（0.991 vs 0.982 vs 0.980）的ROC曲线下面积（AUC）最大。RSF模型在12、18和24个月的BS（0.077 vs 0.095 vs 0.077）小于0.25.
Abstract
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.”
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