Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Published:2018-07-03 Issue:2 Volume:12 Page:206-212
ISSN:2048-8513
Container-title:Clinical Kidney Journal
language:en
Short-container-title:

Author:

Lacson Ronilda C¹²^ORCID,Baker Bowen³,Suresh Harini³,Andriole Katherine¹²,Szolovits Peter³,Lacson Eduardo⁴⁵

Affiliation:

1. Brigham and Women’s Hospital, Boston MA, USA

2. Harvard Medical School, Boston, MA, USA

3. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

4. Dialysis Clinic, Inc., Nashville, TN, USA

5. Tufts Medical Center, Boston, MA, USA

Publisher

Oxford University Press (OUP)

Subject

Transplantation,Nephrology

Link

http://academic.oup.com/ckj/article-pdf/12/2/206/28283498/sfy049.pdf

Reference37 articles.

1. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report;Chobanian;JAMA,2003

2. Role of blood pressure in the development of congestive heart failure. The Framingham study;Kannel;N Engl J Med,1972

3. Blood pressure and survival after myocardial infarction: the Framingham study;Kannel;Am J Cardiol,1980

4. Systolic blood pressure, arterial rigidity, and risk of stroke. The Framingham study;Kannel;JAMA,1981

5. A randomized trial of intensive versus standard blood-pressure control;Group;N Engl J Med,2015

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure;Computers in Biology and Medicine;2024-10

2. Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring;Blood Pressure Monitoring;2024-06-17

3. Application of artificial intelligence in hypertension;Clinical Hypertension;2024-05-01

4. Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study;Psychogeriatrics;2024-03-21

5. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review;BMC Medicine;2024-02-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3