An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation
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 adva...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/1/3 |
| _version_ | 1797241969072668672 |
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| author | Lahiru Theekshana Weerasinghe Rajapaksha Sugandima Mihirani Vidanagamachchi Sampath Gunawardena Vajira Thambawita |
| author_facet | Lahiru Theekshana Weerasinghe Rajapaksha Sugandima Mihirani Vidanagamachchi Sampath Gunawardena Vajira Thambawita |
| author_sort | Lahiru Theekshana Weerasinghe Rajapaksha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:31:46Z |
| format | Article |
| id | doaj.art-e965ad87a0574a359d1a8bb48fb643f0 |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-04-24T18:31:46Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-e965ad87a0574a359d1a8bb48fb643f02024-03-27T13:27:27ZengMDPI AGBioMedInformatics2673-74262023-12-0141344910.3390/biomedinformatics4010003An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical DocumentationLahiru Theekshana Weerasinghe Rajapaksha0Sugandima Mihirani Vidanagamachchi1Sampath Gunawardena2Vajira Thambawita3Department of Computer Science, University of Ruhuna, Matara 81000, Sri LankaDepartment of Computer Science, University of Ruhuna, Matara 81000, Sri LankaDepartment of Physiology, Faculty of Medicine, University of Ruhuna, Galle 80000, Sri LankaDepartment of Holistic Systems, SimulaMet, 0167 Oslo, NorwayCardiac 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.https://www.mdpi.com/2673-7426/4/1/3bed head ticketcardiac arrestclinical documentsdecision tree classification modelearly warning systemdeep learning |
| spellingShingle | Lahiru Theekshana Weerasinghe Rajapaksha Sugandima Mihirani Vidanagamachchi Sampath Gunawardena Vajira Thambawita An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation BioMedInformatics bed head ticket cardiac arrest clinical documents decision tree classification model early warning system deep learning |
| title | An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation |
| title_full | An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation |
| title_fullStr | An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation |
| title_full_unstemmed | An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation |
| title_short | An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation |
| title_sort | open access dataset of hospitalized cardiac arrest patients machine learning based predictions using clinical documentation |
| topic | bed head ticket cardiac arrest clinical documents decision tree classification model early warning system deep learning |
| url | https://www.mdpi.com/2673-7426/4/1/3 |
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