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

ABSTRACT: OBJECTIVES:This study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders. SETTING:The study was conducted in a level-1 trauma centre in southern Taiwan. PARTICIPANTS:Motorcycle 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 MEASURES:The 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. RESULTS:In 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. CONCLUSION:ML 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.

SUBMITTER: Kuo PJ

PROVIDER: S-EPMC5781097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMotorcycles are one of the most commonly used transportation modes in low and middle-income countries. In India, motorized two-wheelers comprise 70% of the total vehicle population, and motorcycle users are considered the most vulnerable road users. It is essential to understand the risky riding behaviour and associated factors among the motorcyclists to develop evidence-based traffic safety programs targeting motorcycle riders. The purpose of the current study was two-fold. First, it aimed to determine the appropriate structure of a modified version of the MRBQ among young riders in Manipal, India. Second, it assessed to what extent MRBQ factors were associated with self-reported crash involvement and violations.MethodsThe motorcycle rider behaviour questionnaire (MRBQ) is a 43-item scale that assesses five aspects of risky motorcycle rider behaviour, i.e., violations, control errors, traffic errors, stunts, and protective equipment. The MRBQ, along with measures of socio-demographic variables and the number of motorcycle crashes, was filled out by 300 young motorcycle riders who were in the age group of 18-25 years and had been riding for at least the past three years (93% males, 92.3% students).ResultsFive factors emerged out of the MRBQ after an exploratory factor analysis: traffic errors, control errors, stunts, protective equipment, and violations. Cronbach's alpha for these factors ranged from .66 to .82. Reports of performing stunts and committing violations were positively associated with self-reported near-crash experiences over the past three months. Riders reporting stunts, violations and using a motorcycle of 125-200 cc reported having received more fines in the last three months. These findings were confirmed in both univariate and multivariate binary logistic regression models.ConclusionThe study assessed the factor structure of a modified version MRBQ and the extracted factors associations with self-reported crash involvement. The factor structure revealed in the current study is consistent with MRBQ factor structures found in other countries. However, the support for a relationship between MRBQ factors and self-reported crashes was less significant. The findings suggest that if replicated by future studies, local policymakers are advised to focus on the five MRBQ factors while planning future interventions to achieve a reduction in the number of road crashes among motorcyclists.

Project description:Purpose: This study aimed to determine the patterns associated with adult mandibular fractures from a Level-I trauma center in southern Taiwan. Methods: The data of adult trauma patients admitted between 1 January 2009 and 31 December 2014 were retrieved from the Trauma Registry System and retrospectively reviewed. Fracture site and cause of injury were categorized into groups for comparison, and corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by multivariate logistic regression. Results: Motorcycle accidents were the most common cause of mandibular fractures (76.3%), followed by falls (10.9%), motor vehicle accidents (4.8%), and being struck by/against objects (4.5%). Of the 503 cases of mandibular fractures, the condylar neck and head were the most common sites (32.0%), followed by the parasymphysis (21.7%), symphysis (19.5%), angle and ramus (17.5%), and body (9.3%). The location of mandibular fractures in patients who had motorcycle accidents was similar to that in all patients. Motor vehicle accidents resulted in a significantly higher number of body fractures (OR 3.3, 95% CI 1.24-8.76, p = 0.017) and struck injury in a significantly higher number of angle and ramus fractures (OR 3.9, 95% CI 1.48-10.26, p = 0.006) compared to motorcycle accidents. The helmet-wearing status and body weight were not associated with the location of mandibular fractures in motorcycle accidents. Conclusions: Our study revealed that the anatomic fracture sites of mandible were specifically related to different etiologies. In southern Taiwan, motorcycle accidents accounted for the major cause of mandibular fractures and were associated with the condylar neck and head as the most frequent fracture sites. In contrast, motor vehicle accidents and struck injuries tended to cause more body fracture as well as angle and ramus fracture compared to motorcycle accidents. Furthermore, the status of helmet-wearing and body weight were not associated with the location of mandible fractures caused by motorcycle accidents.

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

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