Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches

Abstract

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 the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis. Results The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively. Conclusion The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

“…The SAPS II includes seventeen variables, and higher total scores are indicative of greater illness severity [ 26 ]. Prior studies have established an association between SAPS II and an elevated mortality rate among ICU patients [ 27 ]. In addition, we discovered a correlation between urine output and mortality in critically ill patients with CHF combined with CKD.…”

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

“…The SAPS II score comprises seventeen factors, with higher scores indicating illness severity [ 29 ]. Previous research has shown that SAPS II is related to a greater death rate among ICU patients [ 30 ]. In addition, we discovered that respiratory rate is a significant predictor of death in critically ill patients with CKD.…”

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

“…39 Studies have also shown the superiority of Machine Learning/AI techniques including DL 25 over conventional statistical analysis for disease detection and prediction of medical outcomes. 40,41 We assessed the importance of the differentially methylated genes on biological pathways, both to further elucidate the molecular mechanisms of PC and also to determine the biological plausibility of our findings. A high percentage of the 66 epigenetically dysregulated molecular pathways identified (Table S3) was related to cancer.…”

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