Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jun 16:270:1357-1358.
doi: 10.3233/SHTI200440.

Prediction of ROSC After Cardiac Arrest Using Machine Learning

Nan Liu  1   2 , Andrew Fu Wah Ho  2   3 , Pin Pin Pek  2   3 , Tsung-Chien Lu  4 , Pairoj Khruekarnchana  5 , Kyoung Jun Song  6 , Hideharu Tanaka  7 , Ghulam Yasin Naroo  8 , Han Nee Gan  9 , Zhi Xiong Koh  3 , Huei-Ming Ma  4 , Marcus Ong  2   3
Affiliations

Prediction of ROSC After Cardiac Arrest Using Machine Learning

Nan Liu et al. Stud Health Technol Inform. .

Abstract

Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models.

Keywords: Out-of-hospital cardiac arrest; ROSC; machine learning; random forest.

PubMed Disclaimer

LinkOut - more resources