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Use of machine learning techniques to identify risk factors for RV failure in LVAD patients.

作者信息

Nair Nandini

机构信息

Division of Cardiology, Department of Medicine, Texas Tech Health Sciences Center (TTUHSC), Odessa, TX, United States.

出版信息

Front Cardiovasc Med. 2022 Sep 14;9:848789. doi: 10.3389/fcvm.2022.848789. eCollection 2022.

DOI:10.3389/fcvm.2022.848789
PMID:36186964
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9515379/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c4/9515379/85bf5c808258/fcvm-09-848789-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c4/9515379/85bf5c808258/fcvm-09-848789-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c4/9515379/85bf5c808258/fcvm-09-848789-g0001.jpg

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Impact of Right Heart Failure on Clinical Outcome of Left Ventricular Assist Devices (LVAD) Implantation: Single Center Experience.右心衰竭对左心室辅助装置（LVAD）植入临床结局的影响：单中心经验
Healthcare (Basel). 2022 Jan 6;10(1):114. doi: 10.3390/healthcare10010114.
2
A new hemodynamic index to predict late right failure in patients implanted with last generation centrifugal pump.一种新的血流动力学指标，用于预测植入上一代离心泵的患者晚期右心衰竭。
J Card Surg. 2021 Jul;36(7):2355-2364. doi: 10.1111/jocs.15564. Epub 2021 Apr 19.
3
Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.
基于机器学习的种族特异性模型预测 10 年心力衰竭风险的开发和验证：多队列分析。
Circulation. 2021 Jun 15;143(24):2370-2383. doi: 10.1161/CIRCULATIONAHA.120.053134. Epub 2021 Apr 13.
4
Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.新型监测设备与机器学习技术的融合，实现可扩展的心血管管理。
Nat Rev Cardiol. 2021 Feb;18(2):75-91. doi: 10.1038/s41569-020-00445-9. Epub 2020 Oct 9.
5
Predicting the Risk of Right Ventricular Failure in Patients Undergoing Left Ventricular Assist Device Implantation: A Systematic Review.预测接受左心室辅助装置植入术患者右心室衰竭的风险：系统评价。
Circ Heart Fail. 2020 Oct;13(10):e006994. doi: 10.1161/CIRCHEARTFAILURE.120.006994. Epub 2020 Sep 28.
6
Development of novel machine learning model for right ventricular quantification on echocardiography-A multimodality validation study.用于超声心动图右心室定量分析的新型机器学习模型的开发——一项多模态验证研究。
Echocardiography. 2020 May;37(5):688-697. doi: 10.1111/echo.14674. Epub 2020 May 12.
7
Machine Learning in Risk Prediction.风险预测中的机器学习
Hypertension. 2020 May;75(5):1165-1166. doi: 10.1161/HYPERTENSIONAHA.120.13516. Epub 2020 Mar 16.
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Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study.连续可穿戴监测分析预测心力衰竭住院：LINK-HF 多中心研究。
Circ Heart Fail. 2020 Mar;13(3):e006513. doi: 10.1161/CIRCHEARTFAILURE.119.006513. Epub 2020 Feb 25.
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Improving risk prediction in heart failure using machine learning.利用机器学习改善心力衰竭的风险预测。
Eur J Heart Fail. 2020 Jan;22(1):139-147. doi: 10.1002/ejhf.1628. Epub 2019 Nov 12.
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