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Challenges in developing and validating machine learning models for transcatheter aortic valve implantation mortality risk prediction.

作者信息

Kazemian Sina, Issaiy Mahbod, Hosseini Kaveh

机构信息

Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran Heart Center, Kargar St. Jalal al-Ahmad Cross, 1411713138, Tehran, Iran.

Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Imam Khomeini Hospital Complex, Tohid Square, 1419733141, Tehran, Iran.

出版信息

Eur Heart J Digit Health. 2023 Oct 11;5(1):1-2. doi: 10.1093/ehjdh/ztad059. eCollection 2024 Jan.

DOI:10.1093/ehjdh/ztad059
PMID:38264706
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10802814/
Abstract
摘要

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引用本文的文献

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Challenges in developing and validating machine learning models for TAVI mortality risk prediction: reply.开发和验证用于经导管主动脉瓣植入术（TAVI）死亡率风险预测的机器学习模型面临的挑战：回复
Eur Heart J Digit Health. 2023 Nov 8;5(1):3-5. doi: 10.1093/ehjdh/ztad065. eCollection 2024 Jan.

本文引用的文献

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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.
2
Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance.高共线性情况下的变量选择与重要性：多频生物电阻抗预测瘦体重的应用
J Appl Stat. 2020 May 13;48(9):1644-1658. doi: 10.1080/02664763.2020.1763930. eCollection 2021.
3
Baseline ECG and Prognosis After Transcatheter Aortic Valve Implantation: The Role of Interatrial Block.经导管主动脉瓣植入术后的基础心电图与预后：房间隔阻滞的作用。
J Am Heart Assoc. 2020 Nov 17;9(22):e017624. doi: 10.1161/JAHA.120.017624. Epub 2020 Nov 3.
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A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.用于分类预测建模的随机森林变量选择方法比较
Expert Syst Appl. 2019 Nov 15;134:93-101. doi: 10.1016/j.eswa.2019.05.028. Epub 2019 May 23.
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Comparison of variable selection methods for clinical predictive modeling.比较临床预测建模中的变量选择方法。
Int J Med Inform. 2018 Aug;116:10-17. doi: 10.1016/j.ijmedinf.2018.05.006. Epub 2018 May 21.
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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.
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Conditional variable importance for random forests.随机森林的条件变量重要性
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