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

Abstract

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.

From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

“…With the advancements in next-generation sequencing, models trained using these data can also assist the researchers in an accurate diagnosis of the disease and in devising a treatment strategy according to the individual's genetic status. ML and DL models are being developed to help in the early detection of diseases such as cancer, cardiac arrest [5], childhood obesity [6] and many more.…”