摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据News Rx记者在比利时鲁汶的新闻报道，研究表明：“随着人工智能(AI)和机器学习(ML)在心脏结构领域的不断扩展，出现了许多预测TR Anscater主动脉瓣置入术后不良结果的ML模型(TAVI)。我们的目标是识别、描述和严格评估TAVI后不良结果的ML预测模型。”新闻记者从鲁汶大学（KU Leuven）的研究中获得了一句话，“关键目标包括总结模型性能、评估对报道指南的遵守情况和透明度。我们搜索了Pu bMed、SCOPUS和Embase，直到2023年8月。我们选择了预测TAVI结局的已发表的机器李尔宁模型。两名评审员独立筛选ARTIC LES，提取数据采用PRISMA GUID LINES(系统回顾和荟萃分析首选报告项目)对研究质量进行评估，得出总结C统计和偏倚风险评估工具(PROBAST)评估的模型偏倚风险，C统计采用随机效应模型汇总21项研究(118153)。采用Var ious ML算法(76个模型)的患者纳入系统回顾，模型的预测能力变化：11.8%不充分(C-统计量＝1.80)，荟萃分析显示ED对早期死亡率有良好的预测能力(C-统计量＝0.81[95%CI，0.65-0.91])，1年死亡率可接受(C-统计量＝0.76[95%CI，0.67-0.84])，在预测永久性起搏器植入方面具有可接受的性能（C统计量：0.75[95%CI，0.51-0.90]）。TAVI结局的ML模型显示出足够的性能，表明潜在的临床应用价值。
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 reporting from Leuven, Belgium, by News Rx journalists, research stated, "With the expanding integration of artificial i ntelligence (AI) and machine learning (ML) into the structural heart domain, num erous ML models have emerged for the prediction of adverse outcomes following tr anscatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI." The news correspondents obtained a quote from the research from the University o f Leuven (KU Leuven), "Key objectives consisted in summarizing model performance , evaluating adherence to reporting guidelines, and transparency. We searched Pu bMed, SCOPUS, and Embase through August 2023. We selected published machine lear ning models predicting TAVI outcomes. Two reviewers independently screened artic les, extracted data, and assessed the study quality according to the PRISMA guid elines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Out comes included summary C-statistics and model risk of bias assessed with the Pre diction Model Risk of Bias Assessment Tool (PROBAST). C-statistics were pooled u sing a random-effects model. Twenty-one studies (118,153 patients) employing var ious ML algorithms (76 models) were included in the systematic review. Predictiv e ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excelle nt (C-statistic >0.80) performance. Meta-analyses reveal ed excellent predictive performance for early mortality (C-statistic: 0.81 [95 % CI, 0.65-0.91]), acceptable performance for 1-year mortality (C-statistic: 0.76 [95% CI, 0 .67-0.84]), and acceptable performance for predicting permane nt pacemaker implantation (C-statistic: 0.75 [95% CI, 0.51-0.90]). ML models for TAVI outcomes exhibit adequate to excellent performance, suggesting potential clinical utility."
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