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Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches by Mirzakhani, Farzad (author);Sadoughi, Farahnaz (author);Hatami, Mahboobeh (author);Amirabadizadeh, Alireza (author) is a Health Professions article available to read on EtoBox.
## Background A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit. ## Methods This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr’s Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients’ medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under t
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- Author
- Mirzakhani, Farzad (author);Sadoughi, Farahnaz (author);Hatami, Mahboobeh (author);Amirabadizadeh, Alireza (author)
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Health Professions (Health Sciences)