Patient length of stay (LOS) is frequently used by researchers in the field of hospital management as a performance measuring criterion (McDermott & Stock, 2007). Patient LOS is found to be related to the quality of care (Thomas, et al., 1997) and prolonged LOS increases the probability of patients acquiring infections at the hospital. Hence, hospitals provide significant importance to patient LOS to maximize superior performance related rewards and minimize poor care related penalties by the public and private insurance providers. In addition, understanding patient LOS is also necessary for hospitals to meticulously manage their resources. In this research, predictive modeling techniques, including, decision trees, boosted trees, bootstrap forests, are used to predict patient LOS and understand patient attributes that influence patient LOS. Decision trees are tree-based predictive modeling technique, with popularity that is partially attributed to the ease of interpreting the results. On the other hand, boosted tree and bootstrap forest are found to provide high classification and prediction accuracies when the relationship between response and predictor variables is non-linear. Deidentified patient records from a large hospital system in Upstate New York, USA are used for the study in this thesis. The results show that bootstrap forest outperforms decision tree and boosted tree in predicting and classifying patient LOS.
Industrial and Systems Engineering (MS)
Department, Program, or Center
Industrial and Systems Engineering (KGCOE)
Goantiya, Rupansh, "Tree Based Modeling Techniques Applied to Hospital Length of Stay" (2018). Thesis. Rochester Institute of Technology. Accessed from
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