Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.
Seung Min BaikKyung Sook HongDong-Jin ParkPublished in: BMC bioinformatics (2023)
Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients.
Keyphrases
- electronic health record
- sars cov
- coronavirus disease
- cardiovascular events
- deep learning
- clinical decision support
- end stage renal disease
- high resolution
- newly diagnosed
- risk factors
- ejection fraction
- artificial intelligence
- prognostic factors
- peritoneal dialysis
- adverse drug
- magnetic resonance imaging
- machine learning
- coronary artery disease
- dual energy
- contrast enhanced
- neural network