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Precision Medicine in Pulmonary Arterial Hypertension.

作者信息

Leopold Jane A, Maron Bradley A

机构信息

From the Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.

出版信息

Circ Res. 2019 Mar 15;124(6):832-833. doi: 10.1161/CIRCRESAHA.119.314757.

DOI:10.1161/CIRCRESAHA.119.314757
PMID:30870130
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6419751/
Abstract
摘要

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本文引用的文献

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Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients.机器学习算法在成人先天性心脏病中的预后估计和治疗指导：来自单个三级中心的数据，包括 10019 例患者。
Eur Heart J. 2019 Apr 1;40(13):1069-1077. doi: 10.1093/eurheartj/ehy915.
2
Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.利用机器学习在肺动脉高压中发现不同的免疫表型。
Circ Res. 2019 Mar 15;124(6):904-919. doi: 10.1161/CIRCRESAHA.118.313911.
3
Fully Automated Echocardiogram Interpretation in Clinical Practice.临床实践中的全自动超声心动图解读。
Circulation. 2018 Oct 16;138(16):1623-1635. doi: 10.1161/CIRCULATIONAHA.118.034338.
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Emerging Role of Precision Medicine in Cardiovascular Disease.精准医学在心血管疾病中的新兴作用。
Circ Res. 2018 Apr 27;122(9):1302-1315. doi: 10.1161/CIRCRESAHA.117.310782.
5
Network Analysis to Risk Stratify Patients With Exercise Intolerance.网络分析对运动不耐受患者进行风险分层。
Circ Res. 2018 Mar 16;122(6):864-876. doi: 10.1161/CIRCRESAHA.117.312482. Epub 2018 Feb 5.
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PVDOMICS: A Multi-Center Study to Improve Understanding of Pulmonary Vascular Disease Through Phenomics.PVDOMICS：一项通过表型组学改善肺血管疾病理解的多中心研究。
Circ Res. 2017 Oct 27;121(10):1136-1139. doi: 10.1161/CIRCRESAHA.117.311737.
7
Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.三维右心室运动的机器学习可实现肺动脉高压的预后预测：一项心脏磁共振成像研究。
Radiology. 2017 May;283(2):381-390. doi: 10.1148/radiol.2016161315. Epub 2017 Jan 16.
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Precision medicine in cardiology.心脏病学中的精准医学。
Nat Rev Cardiol. 2016 Oct;13(10):591-602. doi: 10.1038/nrcardio.2016.101. Epub 2016 Jun 30.
9
Disease networks. Uncovering disease-disease relationships through the incomplete interactome.疾病网络。通过不完全的相互作用组揭示疾病-疾病关系。
Science. 2015 Feb 20;347(6224):1257601. doi: 10.1126/science.1257601.