Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Mendes, Renata Gonçalves; Souza, César Roberto de; Machado, Maurício de Nassau; Corrêa, Paulo Rogério; Thommazo-Luporini, Luciana Di; Arena, Ross; Myers, Jonathan; Pizzolato, Ednaldo Brigante; Borghi‐Silva, Audrey

doi:10.5114/aoms.2015.48145

Cited by 16 publications

(14 citation statements)

References 34 publications

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“…The NN model was more accurate and outperformed the LR model in predicting in-hospital mortality for patients in critical care [38] and under mechanical ventilation [39]. In this study, although SVM and NN were both expected to handle complex nonlinear relationships between independent and dependent variables better than LR [40,41], the SVM outperformed the NN in the test dataset, and there was no significant difference in AUC values between the LR and SVM. The performance of an ANN depends on the number of parameters, network weights, the selection of an appropriate training algorithm, the type of transfer functions used, and the determination of network size [42].…”

Section: Discussionmentioning

confidence: 87%

Machine Learning Models of Survival Prediction in Trauma Patients

Rau

Chuang

et al. 2019

JCM

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Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

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Section: Discussionmentioning

confidence: 87%

Machine Learning Models of Survival Prediction in Trauma Patients

Rau

Chuang

et al. 2019

JCM

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“…Mendes et al [21] found that neural networks did not outperform LR when predicting mortality in patients after coronary artery bypass grafting. Other studies have suggested an advantage from ML methods over LR.…”

Section: Discussionmentioning

confidence: 99%

Can machine learning improve mortality prediction following cardiac surgery?

Benedetto

Sinha

Lyon

et al. 2020

European Journal of Cardio-Thoracic Surgery

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OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. METHODS A single-centre data set of prospectively collected information from patients undergoing adult cardiac surgery from 1996 to 2017 was split into 70% training set and 30% testing set. Prediction models were developed using neural network, random forest, naive Bayes and retrained LR based on features included in the EuroSCORE. Discrimination was assessed using area under the receiver operating characteristic curve, and calibration analysis was undertaken using the calibration belt method. Model calibration drift was assessed by comparing Goodness of fit χ2 statistics observed in 2 equal bins from the testing sample ordered by procedure date. RESULTS A total of 28 761 cardiac procedures were performed during the study period. The in-hospital mortality rate was 2.7%. Retrained LR [area under the receiver operating characteristic curve 0.80; 95% confidence interval (CI) 0.77–0.83] and random forest model (0.80; 95% CI 0.76–0.83) showed the best discrimination. All models showed significant miscalibration. Retrained LR proved to have the weakest calibration drift. CONCLUSIONS Our findings do not support the hypothesis that machine learning methods provide advantage over LR model in predicting operative mortality after cardiac surgery.

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“…Second, unlike the LR model, the ANN model does not have a recognized model of input variable access and elimination. Third, as a result of their structure, ANN models do not provide any medical explanation pertaining to each independent variable; thus, the hypothesis test methods, confidence intervals, and other issues require additional research [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Guo

Zhou

et al. 2018

PLoS Negl Trop Dis

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BackgroundIn order to better assist medical professionals, this study aimed to develop and compare the performance of three models—a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model—to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.Methodology/Principal findingsSchistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient’s outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.Conclusions/SignificancePredictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.

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Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Cited by 16 publications

References 34 publications

Machine Learning Models of Survival Prediction in Trauma Patients

Machine Learning Models of Survival Prediction in Trauma Patients

Can machine learning improve mortality prediction following cardiac surgery?

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

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