Cardiac Arrest and Cardiopulmonary Resuscitation

Tang, Wanchun; Weil, Max Harry

doi:10.1016/b978-032304841-5.50003-0

Cited by 3 publications

(3 citation statements)

References 134 publications

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“…Dysfunction in the heart's conduction system frequently leads to cardiac arrest, which results in the heart's inability to pump blood efficiently. Ventricular fibrillation is the primary cause of cardiac arrest in 65-80% of cases [1]. Numerous heart-related conditions can lead to cardiovascular complications, including coronary artery disease, cardiomyopathy, inherited conditions, congenital heart disease, heart valve disease, acute myocarditis, and conduction disorders, such as long QT syndrome.…”

Section: Introductionmentioning

confidence: 99%

An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation

Rajapaksha,

Vidanagamachchi,

Gunawardena

et al. 2023

BioMedInformatics

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Cardiac arrest is a sudden loss of heart function with serious consequences. In developing countries, healthcare professionals use clinical documentation to track patient information. These data are used to predict the development of cardiac arrest. We published a dataset through open access to advance the research domain. While using this dataset, our work revolved around generating and utilizing synthetic data by harnessing the potential of synthetic data vaults. We conducted a series of experiments by employing state-of-the-art machine-learning techniques. These experiments aimed to assess the performance of our developed predictive model in identifying the likelihood of developing cardiac arrest. This approach was effective in identifying the risk of cardiac arrest in in-patients, even in the absence of electronic medical recording systems. The study evaluated 112 patients who had been transferred from the emergency treatment unit to the cardiac medical ward. The developed model achieved 96% accuracy in predicting the risk of developing cardiac arrest. In conclusion, our study showcased the potential of leveraging clinical documentation and synthetic data to create robust predictive models for cardiac arrest. The outcome of this effort could provide valuable insights and tools for healthcare professionals to preemptively address this critical medical condition.

show abstract

Section: Introductionmentioning

confidence: 99%

An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation

Rajapaksha,

Vidanagamachchi,

Gunawardena

et al. 2023

BioMedInformatics

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“…Sudden cardiac arrest (SCA) refers to the sudden loss of cardiac mechanical activity due to ventricular fibrillation or asystole [1]. Across the world, SCA is the most common cause of cardiac death and is responsible for 15-20% of all deaths [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Survival-to-discharge rates of out-of-hospital cardiac arrests have also improved over time [11,12]. Eighty percent of SCA have a cardiac cause, with majority of these cases being due to coronary artery disease, and influenza may affect SCA rates by triggering AMI [1].…”

Section: Introductionmentioning

confidence: 99%

Burden of influenza in adults with cardiac arrest admissions in Australia

Moa

Tan

Hutchinson

et al. 2022

International Journal of Cardiology

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An Open-Access Data Set of Hospitalized Cardiac Arrest Patients: Machine Learning-Based Predictions Using Clinical Documentation

Rajapaksha,

Vidanagamachchi,

Gunawardena

et al. 2023

Preprint

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Cardiac arrest is a sudden loss of heart function with serious consequences. In developing countries, healthcare professionals use clinical documentation to track patient information. These data can be used to predict developing cardiac arrests. We are publishing the data set through open access to advance the research domain. Based on this data set, our work revolves around generating and utilizing synthetic data by harnessing the potential of synthetic data vaults. We conducted a series of experiments by employing state-of-the-art machine learning techniques. These experiments were aimed to assess the performance of our developed predictive model in identifying the likelihood of developing cardiac arrests. This approach is effective in identifying the risk of cardiac arrest in inpatients, even in the absence of electronic medical recording systems. The study evaluated 112 patients who were transferred from the Emergency Treatment Unit to the Cardiac Medical Ward. The developed model achieved 96\% accuracy in predicting the risk of developing cardiac arrest. In conclusion, our study showcases the potential of leveraging clinical documentation and synthetic data to create robust predictive models for cardiac arrest. The outcome of this effort will provide valuable insights and tools for healthcare professionals to preemptively address this critical medical condition.

show abstract

Cardiac Arrest and Cardiopulmonary Resuscitation

Cited by 3 publications

References 134 publications

An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation

An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation

Burden of influenza in adults with cardiac arrest admissions in Australia

An Open-Access Data Set of Hospitalized Cardiac Arrest Patients: Machine Learning-Based Predictions Using Clinical Documentation

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