Improving clinical trial efficiency using a machine learning‐based risk score to enrich study populations-Reference-Cited by-同舟云学术

Improving clinical trial efficiency using a machine learning‐based risk score to enrich study populations

Published:2022-05-22 Issue:8 Volume:24 Page:1418-1426
ISSN:1388-9842
Container-title:European Journal of Heart Failure
language:en
Short-container-title:European J of Heart Fail

Author:

Jering Karola S.¹^ORCID,Campagnari Claudio²,Claggett Brian¹,Adler Eric³,Klein Liviu⁴,Ahmad Faraz S.⁵,Voors Adriaan A.⁶,Solomon Scott¹,Yagil Avi³⁷,Greenberg Barry³

Affiliation:

1. Cardiovascular Division Brigham and Women's Hospital and Harvard Medical School Boston MA USA

2. Physics Department University of California Santa Barbara CA USA

3. Cardiology Department University of California San Diego CA USA

4. Division of Cardiology University of California San Francisco CA USA

5. Division of Cardiology, Department of Medicine Northwestern University Feinberg School of Medicine Chicago IL USA

6. University of Groningen, University Medical Center Groningen Groningen The Netherlands

7. Physics Department University of California San Diego CA USA

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ejhf.2528

Reference32 articles.

1. Heart Disease and Stroke Statistics—2021 Update

2. Global mortality variations in patients with heart failure: results from the International Congestive Heart Failure (INTER-CHF) prospective cohort study

3. Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study

4. Postponement of Death by Pharmacological Heart Failure Treatment: A Meta-Analysis of Randomized Clinical Trials

5. How do patients with heart failure with preserved ejection fraction die?

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