A new survival status prediction system for severe trauma patients based on a multiple classifier system

Sanz, José Antonio; Paternain, Daniel; Galar, Mikel; Fernández, Javier; Reyero, Diego; Belzunegui, Toms

doi:10.1016/j.cmpb.2017.02.011

Cited by 17 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…C4.5 is an algorithm used to generate a decision tree developed by Quinlan [ 18 ], and is an extension of Quinlan's earlier ID3 algorithm. C4.5 is often referred to as a statistical classifier [ 19 ], and it is a widely used classifier to face real world problems [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms

Kuo

Chen

et al. 2018

Healthc Inform Res

View full text Add to dashboard Cite

ObjectivesThe aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion.MethodsA data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1.ResultsFive hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904.ConclusionsOur study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.

show abstract

Section: Methodsmentioning

confidence: 99%

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms

Kuo

Chen

et al. 2018

Healthc Inform Res

View full text Add to dashboard Cite

show abstract

“…In addition, the imputation of physiological and laboratory data collected from the time of arrival at the emergency department cannot reflect the dynamic changes in haemodynamic and metabolic variables of the patients with trauma when resuscitation is possible. Furthermore, other DT-related methods, such as DT by C4.5, 60 combined classifiers of LR and DT by C4.5, 48 and random forest, 61 have extremely satisfying performance in dealing with the classification problem. However, these techniques were not investigated in this study.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, because the geometric mean can provide a good trade-off between sensitivity and specificity in a manner that a better accuracy in both classes leads to a larger value, it was calculated in this study according to the methods used by Sanz et al . 48 …”

Section: Methodsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Classification is one of the most well-known examples of Machine Learning, since many real-world problems can be formulated as classification problems [1], [2]. Specifically, supervised classification consists of learning a classifier from labeled data in such a way that it is able to correctly classify new examples (also called instances) that were not taken into consideration during the learning step [3].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Fuzzy Measure Learning for Classifier Ensembles From Coalitions Performance

et al. 2020

Self Cite

View full text Add to dashboard Cite

In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: how to create a diverse set of classifiers and how to combine their outputs. This work focuses on the latter task. More specifically, we focus on the usage of aggregation functions based on fuzzy measures, such as the Sugeno and Choquet integrals, since they allow to model the coalitions and interactions among the members of the ensemble. In this scenario the challenge is how to construct a fuzzy measure that models the relations among the members of the ensemble. We focus on unsupervised methods for fuzzy measure construction, review existing alternatives and categorize them depending on their features. Furthermore, we intend to address the weaknesses of previous alternatives by proposing a new construction method that obtains the fuzzy measure directly evaluating the performance of each possible subset of classifiers, which can be efficiently computed. To test the usefulness of the proposed fuzzy measure, we focus on the application of ensembles for imbalanced datasets. We consider a set of 66 imbalanced datasets and develop a complete experimental study comparing the reviewed methods and our proposal.

show abstract

A new survival status prediction system for severe trauma patients based on a multiple classifier system

Cited by 17 publications

References 42 publications

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms

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

Unsupervised Fuzzy Measure Learning for Classifier Ensembles From Coalitions Performance

Contact Info

Product

Resources

About