Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention-Reference-Cited by-同舟云学术

Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention

Published:2019-07-10 Issue:7 Volume:2 Page:e196835
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Mortazavi Bobak J.¹²³⁴,Bucholz Emily M.⁵⁶,Desai Nihar R.³⁴,Huang Chenxi³⁴,Curtis Jeptha P.³⁴,Masoudi Frederick A.⁷,Shaw Richard E.⁸,Negahban Sahand N.⁹,Krumholz Harlan M.³⁴¹⁰

Affiliation:

1. Department of Computer Science and Engineering, Texas A&M University, College Station

2. Center for Remote Health Technologies and Systems, Texas A&M University, College Station

3. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut

4. Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut

5. Division of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut

6. Now with the Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts

7. Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora

8. Division of Cardiology, Department of Medicine, California Pacific Medical Center, Sutter Health, San Francisco

9. Department of Statistics, Yale University, New Haven, Connecticut

10. Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

http:///jamanetworkopen/article-pdf/2/7/e196835/17695769/mortazavi_2019_oi_190275.pdf

Reference21 articles.

1. Bleeding in patients undergoing percutaneous coronary intervention: the development of a clinical risk algorithm from the National Cardiovascular Data Registry.;Mehta;Circ Cardiovasc Interv,2009

2. Association between bleeding, blood transfusion, and costs among patients with non–ST-segment elevation acute coronary syndromes.;Rao;Am Heart J,2008

3. An updated bleeding model to predict the risk of post-procedure bleeding among patients undergoing percutaneous coronary intervention: a report using an expanded bleeding definition from the National Cardiovascular Data Registry CathPCI Registry.;Rao;JACC Cardiovasc Interv,2013

4. The prevalence and outcomes of transradial percutaneous coronary intervention for ST-segment elevation myocardial infarction: analysis from the National Cardiovascular Data Registry (2007 to 2011).;Baklanov;J Am Coll Cardiol,2013

5. Development and validation of a bleeding risk model for patients undergoing elective percutaneous coronary intervention.;Montalescot;Int J Cardiol,2011

Cited by 61 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. Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries;BMC Surgery;2024-05-10

2. Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation;Journal of the American Heart Association;2024-02-06

3. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions;European Heart Journal;2024-01-17

4. The prevalence of bleeding after percutaneous coronary interventions: A systematic review and meta-analysis;Indian Heart Journal;2024-01

5. Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases;The Anatolian Journal of Cardiology;2024