Comput. Biol. Med. - Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

Tópicos

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Resumo

CKGROUND: The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children.METHODS: We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity.RESULTS: The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination.CONCLUSION: Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks.

Resumo Limpo

ckground accur predict surgic risk import patient physician logist regress lr model typic use estim risk howev field data mine machinelearn mani altern classif predict algorithm develop studi aim compar perform lr sever data mine algorithm predict day surgic morbid childrenmethod use nation surgic qualiti improv programpediatr dataset compar perform lr model assum linear addit simpl lr model lr model incorpor restrict cubic spline interact flexibl lr model support vector machin random forest boost classif tree predict surgic morbidityresult ensemblebas method show signific higher accuraci sensit specif ppv npv simpl lr model howev none model perform better flexibl lr model term aforement measur model calibr discriminationconclus support vector machin random forest boost classif tree show better perform lr predict pediatr surgic morbid valid flexibl lr model deriv studi use assist clinic decisionmak base patientspecif surgic risk

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