Affiliation:
1. Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center
2. Center for Biostatistics, Department of Biomedical Informatics, College of Medicine
3. Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH
4. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH
Abstract
Objectives
For population databases, multivariable regressions are established analytical standards. The utilization of machine learning (ML) in population databases is novel. We compared conventional statistical methods and ML for predicting mortality in biliary acute pancreatitis (biliary AP).
Methods
Using the Nationwide Readmission Database (2010–2014), we identified patients (age ≥18 years) with admissions for biliary AP. These data were randomly divided into a training (70%) and test set (30%), stratified by the outcome of mortality. The accuracy of ML and logistic regression models in predicting mortality was compared using 3 different assessments.
Results
Among 97,027 hospitalizations for biliary AP, mortality rate was 0.97% (n = 944). Predictors of mortality included severe AP, sepsis, increasing age, and nonperformance of cholecystectomy. Assessment metrics for predicting the outcome of mortality, the scaled Brier score (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.16–0.33 vs 0.18; 95% CI, 0.09–0.27), F-measure (OR, 43.4; 95% CI, 38.3–48.6 vs 40.6; 95% CI, 35.7–45.5), and the area under the receiver operating characteristic (OR, 0.96; 95% CI, 0.94–0.97 vs 0.95; 95% CI, 0.94–0.96) were comparable between the ML and logistic regression models, respectively.
Conclusions
For population databases, traditional multivariable analysis is noninferior to ML-based algorithms in predictive modeling of hospital outcomes for biliary AP.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Endocrinology,Hepatology,Endocrinology, Diabetes and Metabolism,Internal Medicine