Use of machine learning models to predict neurologically intact survival for advanced age adults following out‐of‐hospital cardiac arrest

Authors

Ryan L. Melvin (ORCID: https://orcid.org/0000-0001-6770-3841)
Ryan C. Godwin (ORCID: https://orcid.org/0000-0001-6590-3970)
Ryan A. Coute (ORCID: https://orcid.org/0000-0003-2915-628X)
Brian Harris Nathanson (ORCID: https://orcid.org/0000-0001-5079-1814)
Dylana Adams
Kameshwari Soundararajan
Timothy J. Mader

Institutions

University of Massachusetts Chan Medical School (US)
University of Alabama at Birmingham (US)
Optim Design (United States) (US)
Baystate Health (US)
Center for Surveillance, Epidemiology, and Laboratory Services (US)

Publication Details

Journal: Academic Emergency Medicine
Published: 2024-09-19
DOI: https://doi.org/10.1111/acem.15018
Citations: 3
Primary Topic: Cardiac Arrest and Resuscitation
Type: article
Field-Weighted Citation Impact: 2.07

Funders

American Heart Association
National Heart, Lung, and Blood Institute

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article

Use of machine learning models to predict neurologically intact survival for advanced age adults following out‐of‐hospital cardiac arrest

Ryan L. Melvin, Ryan C. Godwin, Ryan A. Coute, Brian Harris Nathanson et al.

Sep 19, 2024

3 citations

Academic Emergency Medicine

Cardiac Arrest and Resuscitation

2.07

article

Use of machine learning models to predict neurologically intact survival for advanced age adults following out‐of‐hospital cardiac arrest

Ryan L. Melvin, Ryan C. Godwin, Ryan A. Coute, Brian Harris Nathanson, Dylana Adams, Kameshwari Soundararajan, Timothy J. Mader

September 19, 2024article en

3 citations

Abstract

No abstract available for this paper.

Academic Emergency MedicineVol. 32(2)

University of Massachusetts Chan Medical School (US), University of Alabama at Birmingham (US), Optim Design (United States) (US), Baystate Health (US), Center for Surveillance, Epidemiology, and Laboratory Services (US)

American Heart Association, National Heart, Lung, and Blood Institute

Openalex Percentile: Top 13%

Cardiac Arrest and Resuscitation

2.07

DOI Open Access PDF OpenAlex

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