Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages
A burn injury is a disastrous trauma and can have wide-ranging impacts on burn patients, their families, and society. Burn patients generally experience long hospital stays, and the accurate prediction of the length of those stays has strong implications for healthcare resource management and service delivery. In addition to prediction accuracy, the timing of length of hospital stay (LOS) predictions is also relevant, because LOS predictions during earlier clinical stages (e.g., admission) can provide an important component for service and resource planning as well as patient and family counseling, whereas LOS predictions at later clinical stages (e.g., post-treatment) can support resource utilization reviews and cost controls. This study evaluates the effectiveness of LOS predictions for burn patients during three different clinical stages: admission, acute, and post-treatment. In addition, we compare the prediction effectiveness of two artificial intelligence (AI)-based prediction techniques (i.e., model-tree-based regression and support vector machine regression), using linear regression analysis as our performance benchmark. On the basis of 1080 burn cases collected in Taiwan, the empirical evaluation suggests that the accuracy of LOS predictions at the acute stage does not improve compared with those during the admission stage, but LOS predictions at the post-treatment stage are significantly more accurate. Moreover, the AI-based prediction techniques, especially support vector machine regression, appear more effective than the regression technique for LOS predictions for burn patients across stages.
burn patients length of hospital stay (los) los prediction model-tree-based regression support vector machine regression healthcare decision support healthcare management