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Challenges in developing and validating machine learning models for TAVI mortality risk prediction: reply.

作者信息

Leha Andreas, Huber Cynthia, Friede Tim, Bauer Timm, Beckmann Andreas, Bekeredjian Raffi, Bleiziffer Sabine, Herrmann Eva, Möllmann Helge, Walther Thomas, Beyersdorf Friedhelm, Hamm Christian, Künzi Arnaud, Windecker Stephan, Stortecky Stefan, Kutschka Ingo, Hasenfuß Gerd, Ensminger Stephan, Frerker Christian, Seidler Tim

机构信息

Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.

DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Robert-Koch str. 40, 37075 Göttingen, Germany.

出版信息

Eur Heart J Digit Health. 2023 Nov 8;5(1):3-5. doi: 10.1093/ehjdh/ztad065. eCollection 2024 Jan.

DOI:10.1093/ehjdh/ztad065
PMID:38264698
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10802823/
Abstract
摘要

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本文引用的文献

1
Challenges in developing and validating machine learning models for transcatheter aortic valve implantation mortality risk prediction.开发和验证用于经导管主动脉瓣植入术死亡风险预测的机器学习模型所面临的挑战。
Eur Heart J Digit Health. 2023 Oct 11;5(1):1-2. doi: 10.1093/ehjdh/ztad059. eCollection 2024 Jan.
2
Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores.经导管主动脉瓣植入术死亡率风险的可解释机器学习模型的开发与验证：TAVI风险机器评分
Eur Heart J Digit Health. 2023 Mar 17;4(3):225-235. doi: 10.1093/ehjdh/ztad021. eCollection 2023 May.
3
Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations.人工智能/机器学习方法在心血管医学中的应用：一项带有建议的系统评价
Eur Heart J Digit Health. 2021 Jun 8;2(3):424-436. doi: 10.1093/ehjdh/ztab054. eCollection 2021 Sep.
4
Incidence, Predictors, and Implications of Permanent Pacemaker Requirement After Transcatheter Aortic Valve Replacement.经导管主动脉瓣置换术后永久起搏器需求的发生率、预测因素及其影响。
JACC Cardiovasc Interv. 2021 Jan 25;14(2):115-134. doi: 10.1016/j.jcin.2020.09.063.
5
The uncertainty with using risk prediction models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care.使用风险预测模型进行个体决策的不确定性：以英国初级保健中心血管疾病预测为例的队列研究
BMC Med. 2019 Jul 17;17(1):134. doi: 10.1186/s12916-019-1368-8.
6
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.超越心血管风险预测中的回归技术：应用机器学习解决分析挑战。
Eur Heart J. 2017 Jun 14;38(23):1805-1814. doi: 10.1093/eurheartj/ehw302.
7
A plea for neutral comparison studies in computational sciences.呼吁在计算科学中进行中立的对比研究。
PLoS One. 2013 Apr 24;8(4):e61562. doi: 10.1371/journal.pone.0061562. Print 2013.
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Conditional variable importance for random forests.随机森林的条件变量重要性
BMC Bioinformatics. 2008 Jul 11;9:307. doi: 10.1186/1471-2105-9-307.
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Regression modelling of diagnostic likelihood ratios for the evaluation of medical diagnostic tests.用于评估医学诊断试验的诊断似然比的回归建模。
Biometrics. 1998 Jun;54(2):444-52.