External Validation of Two Classification and Regression Tree Models to Predict the Outcome of Inpatient Cardiopulmonary Resuscitation

Guilbault, Ryan; Ohlsson, Marcus; Afonso, Anna M.; Ebell, Mark H.

doi:10.1177/0885066616686924

Cited by 16 publications

(17 citation statements)

References 24 publications

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“…There are many models, including C4.5 and C5.0, DTs, ID3s, CART, and chi-square automatic interaction detector DTs (CHAIDs), which could be used to construct DT models [ 26 , 28 ]. CART analysis is an innovative DT model in which several predictive variables are crucial in the identification of patients at different levels of risk, in various medical fields, through progressive binary splits, to develop a model for better prediction and clinical decision-making [ 30 , 31 , 32 ]. Among these methods, CART analysis is conducted based on the combination of nonparametric and nonlinear variables for recursive partitioning analysis [ 30 , 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…CART analysis is an innovative DT model in which several predictive variables are crucial in the identification of patients at different levels of risk, in various medical fields, through progressive binary splits, to develop a model for better prediction and clinical decision-making [ 30 , 31 , 32 ]. Among these methods, CART analysis is conducted based on the combination of nonparametric and nonlinear variables for recursive partitioning analysis [ 30 , 31 , 32 ]. Approaches with different DT models may provide a model with similar predictive power but with the selection of different kinds of variables as nodes.…”

Section: Discussionmentioning

confidence: 99%

“…With the repetition of the same process on each predictor in the model, the CART identifies the best overall split by iteratively testing all the possible splits and producing the greatest reduction in impurity [ 30 , 31 ]. The CART analysis was performed recursively, in this manner, until the specified stopping criteria were reached, a specified number of nodes were created, or a further reduction in node impurity became impossible [ 30 , 31 , 32 ]. In order to generate a sequence of simpler trees, each of which is a candidate for the appropriately-fit final tree, the method of “cost-complexity” pruning is used.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System

Rau

Chien

et al. 2017

IJERPH

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Background: In contrast to patients with traumatic subarachnoid hemorrhage (tSAH) in the presence of other types of intracranial hemorrhage, the prognosis of patients with isolated tSAH is good. The incidence of mortality in these patients ranges from 0–2.5%. However, few data or predictive models are available for the identification of patients with a high mortality risk. In this study, we aimed to construct a model for mortality prediction using a decision tree (DT) algorithm, along with data obtained from a population-based trauma registry, in a Level 1 trauma center. Methods: Five hundred and forty-five patients with isolated tSAH, including 533 patients who survived and 12 who died, between January 2009 and December 2016, were allocated to training (n = 377) or test (n = 168) sets. Using the data on demographics and injury characteristics, as well as laboratory data of the patients, classification and regression tree (CART) analysis was performed based on the Gini impurity index, using the rpart function in the rpart package in R. Results: In this established DT model, three nodes (head Abbreviated Injury Scale (AIS) score ≤4, creatinine (Cr) <1.4 mg/dL, and age <76 years) were identified as important determinative variables in the prediction of mortality. Of the patients with isolated tSAH, 60% of those with a head AIS >4 died, as did the 57% of those with an AIS score ≤4, but Cr ≥1.4 and age ≥76 years. All patients who did not meet the above-mentioned criteria survived. With all the variables in the model, the DT achieved an accuracy of 97.9% (sensitivity of 90.9% and specificity of 98.1%) and 97.7% (sensitivity of 100% and specificity of 97.7%), for the training set and test set, respectively. Conclusions: The study established a DT model with three nodes (head AIS score ≤4, Cr <1.4, and age <76 years) to predict fatal outcomes in patients with isolated tSAH. The proposed decision-making algorithm may help identify patients with a high risk of mortality.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System

Rau

Chien

et al. 2017

IJERPH

View full text Add to dashboard Cite

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“…CART repeated this same process on each predictor in the model, thus identifying the best split by iteratively testing all possible splits and producing the most significant reduction in impurity. 38–40 CART proceeded recursively in this manner until the specified stopping criteria were met, a specified number of nodes were created or a further reduction in node impurity was obtained. 38–40 …”

Section: Methodsmentioning

confidence: 99%

“…In addition, it is an innovative DT model in which several predictive variables are used in identifying high-risk patients in various medical fields through progressive binary splits to develop prediction models and to enable better prediction and clinical decision-making. 38–40 …”

Section: Introductionmentioning

confidence: 99%

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

Kuo

Chien

et al. 2018

BMJ Open

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ObjectivesThis study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders.SettingThe study was conducted in a level-1 trauma centre in southern Taiwan.ParticipantsMotorcycle riders who were hospitalised between January 2009 and December 2015 were classified into a training set (n=6306) and test set (n=946). Using the demographic information, injury characteristics and laboratory data of patients, logistic regression (LR), support vector machine (SVM) and decision tree (DT) analyses were performed to determine the mortality of individual motorcycle riders, under different conditions, using all samples or reduced samples, as well as all variables or selected features in the algorithm.Primary and secondary outcome measuresThe predictive performance of the model was evaluated based on accuracy, sensitivity, specificity and geometric mean, and an analysis of the area under the receiver operating characteristic curves of the two different models was carried out.ResultsIn the training set, both LR and SVM had a significantly higher area under the receiver operating characteristic curve (AUC) than DT. No significant difference was observed in the AUC of LR and SVM, regardless of whether all samples or reduced samples and whether all variables or selected features were used. In the test set, the performance of the SVM model for all samples with selected features was better than that of all other models, with an accuracy of 98.73%, sensitivity of 86.96%, specificity of 99.02%, geometric mean of 92.79% and AUC of 0.9517, in mortality prediction.ConclusionML can provide a feasible level of accuracy in predicting the mortality of motorcycle riders. Integration of the ML model, particularly the SVM algorithm in the trauma system, may help identify high-risk patients and, therefore, guide appropriate interventions by the clinical staff.

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Establishment and validation of a recursive partitioning analysis based prognostic model for guiding re-radiotherapy in locally recurrent nasopharyngeal carcinoma patients

Sun

Zhu

Wen

et al. 2022

Radiotherapy and Oncology

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External Validation of Two Classification and Regression Tree Models to Predict the Outcome of Inpatient Cardiopulmonary Resuscitation

Abstract: Two CART models validated well in a different population, displaying similar discrimination and classification accuracy compared to the original population. Although additional validation in larger populations is desirable before widespread adoption, these results are very encouraging.

Cited by 16 publications

References 24 publications

Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System

Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

Establishment and validation of a recursive partitioning analysis based prognostic model for guiding re-radiotherapy in locally recurrent nasopharyngeal carcinoma patients

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