Predictive Analysis for Trauma Patient Readmission Database PDF Download

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Predictive Analysis for Trauma Patient Readmission Database

Predictive Analysis for Trauma Patient Readmission Database PDF Author: Weiwei Jiao
Publisher:
ISBN:
Category : Hospitals
Languages : en
Pages : 42

Book Description
Introduction: Identifying the key elements associated with hospital readmission is critical in terms of controlling the cost for hospitals and improving the care quality for patients. Our goal is to compare three different statistical models of predicting readmission rate in pediatric trauma patients and identify important risk factors. Methods: Logistic regression, random forest and support vector machine are popular statistical models for predicting binary outcomes. We apply these three methods to the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD) 2013-2014 to compare their predictive performance for readmission. Results: The Support Vector Machine method with linear function has the greatest mean AUC (0.6724) across 10-fold cross validation in the training set. The logistic regression model has the greatest AUC value (0.6862) in the validation set. Support Vector Machine with linear function (AUC=0.6842) has the lowest misclassification rate and highest sensitivity in the validation set. Conclusions: Pediatric trauma patients have a low readmission risk. The key factors of readmission are CCS diagnosis, age and mechanism of trauma.
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