ArticleBMC medical informatics and decision making2022
Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Integrating host mRNA signatures and clinical trajectories with machine learning for risk stratification and survival prediction in sepsis: an ICU-based prospective cohort study.BMC infectious diseases · 2026Article
- Review
- Development and external validation of a machine learning model for predicting chronic critical illness in ICU patients with acute pancreatitis.BMC medical informatics and decision making · 2026Article
- Developing a Mortality Risk Score in Intensive Care Units of Referral Hospitals in Bahir Dar City, Ethiopia: A Prognostic Study.Health science reports · 2026Article
- Review
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.Archives of academic emergency medicine · 2025Article
- Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.Biomedicines · 2024Review
- Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint.International journal of molecular sciences · 2024Article
- Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.Renal failure · 2024Article
- Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2024Article
- Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.Renal failure · 2023Article
- Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study.Cancer medicine · 2023Article
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4 authors.
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Abstract
backgroundA 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.
methodsThis 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.
resultsThe 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.
conclusionThe 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.
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