ArticleComputers in biology and medicine2015
Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.
Article in Computers in biology and medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Is inpatient monitoring necessary in pediatric patients with comorbidities following routine dental procedures? A retrospective analysis.Oral and maxillofacial surgery · 2026Article
- Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database.Journal of the American College of Surgeons · 2026Article
- Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.American journal of surgery · 2021Article
- An Exome-wide Association Study for Type 2 Diabetes-Attributed End-Stage Kidney Disease in African Americans.Kidney international reports · 2018Article
- A novel risk classification system for 30-day mortality in children undergoing surgery.PloS one · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe 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.
methodsWe 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.
resultsThe 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.
conclusionSupport 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.
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