A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery

Cao, Yang; Fang, Xin; Ottosson, Johan; Näslund, Erik; Stenberg, Erik

doi:10.1101/376038

Cited by 16 publications

(25 citation statements)

References 33 publications

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“…The performance of a predictive model was evaluated using accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve. Terminology and derivations of the metrics have been given in detail elsewhere [24]. Model success was defined as an area under the ROC curve (AUC) greater than 0.7 [25].…”

Section: Predictive Models and Validationmentioning

confidence: 99%

“…There are other machine learning algorithms available, such as discriminant analysis, decision tree, K-nearest neighbor, support vector machine, and multilayer perceptron, for supervised classification problems [24]. The reasons for using and comparing logistic regression to random forest in the current study are: a) Logistic regression is the most widely used method in diagnostic tests and prediction studies for binary outcomes in medical sciences.…”

Section: Predictive Model Selectionmentioning

confidence: 99%

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The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

Cao

Bass

Ahl

et al. 2020

BMC Med Inform Decis Mak

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Background: Geriatric patients frequently undergo emergency general surgery and accrue a greater risk of postoperative complications and fatal outcomes than the general population. It is highly relevant to develop the most appropriate care measures and to guide patient-centered decision-making around end-of-life care. Portsmouth-Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM) has been used to predict mortality in patients undergoing different types of surgery. In the present study, we aimed to evaluate the relative importance of the P-POSSUM score for predicting 90-day mortality in the elderly subjected to emergency laparotomy from statistical aspects. Methods: One hundred and fifty-seven geriatric patients aged ≥65 years undergoing emergency laparotomy between January 1st, 2015 and December 31st, 2016 were included in the study. Mortality and 27 other patient characteristics were retrieved from the computerized records of Örebro University Hospital in Örebro, Sweden. Two supervised classification machine methods (logistic regression and random forest) were used to predict the 90-day mortality risk. Three scalers (Standard scaler, Robust scaler and Min-Max scaler) were used for variable engineering. The performance of the models was evaluated using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Importance of the predictors were evaluated using permutation variable importance and Gini importance. Results: The mean age of the included patients was 75.4 years (standard deviation =7.3 years) and the 90-day mortality rate was 29.3%. The most common indication for surgery was bowel obstruction occurring in 92 (58.6%) patients. Types of postoperative complications ranged between 7.0-36.9% with infection being the most common type. Both the logistic regression and random forest models showed satisfactory performance for predicting 90-day mortality risk in geriatric patients after emergency laparotomy, with AUCs of 0.88 and 0.93, respectively. Both models had an accuracy > 0.8 and a specificity ≥0.9. P-POSSUM had the greatest relative importance for predicting 90-day mortality in the logistic regression model and was the fifth important predictor in the random forest model. No notable change was found in sensitivity analysis using different variable engineering methods with P-POSSUM being among the five most accurate variables for mortality prediction.

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Section: Predictive Models and Validationmentioning

confidence: 99%

Section: Predictive Model Selectionmentioning

confidence: 99%

The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

Cao

Bass

Ahl

et al. 2020

BMC Med Inform Decis Mak

Self Cite

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show abstract

“…Performance of the GBN was evaluated using the mean squared error (MSE) in view of the existence of zero values in the outcome variables [ 32 ]. MSE from the min-max normalized scores (between 0 and 1) was used to compare the results from the GBN and those from the previous multivariable linear regression and the CNN [ 33 ]. Performance of the DBN and MLR was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve.…”

Section: Methodsmentioning

confidence: 99%

“…Performance of the DBN and MLR was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Terminology and derivations of the metrics were given in detail elsewhere [ 33 ]. A successful prediction model for comorbidities was defined as with an area under the ROC curve (AUC) greater than 0.7 [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

Cao

Raoof

Szabó

et al. 2020

JCM

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Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.

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“…The rise of machine learning is driven by the ability to process “big data” and the need to deliver the best possible value- and evidence-based care. The utility of artificial intelligence (AI) coupled with machine learning, has generated much interest and many studies in clinical medicine [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [ 80 , 81 , 82 ].…”

Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

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A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery

Cited by 16 publications

References 33 publications

The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

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