Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.69
A model to predict the Length of Stay (LOS) for hospitalized patients can be an effective tool for healthcare providers. Such a model will enable early interventions to prevent complications and prolonged LOS and also enable more efficient utilization of manpower and facilities in hospitals. In this paper, we propose an approach for Predicting Hospital Length of Stay (PHLOS) using a multi-tiered data mining approach. In this paper we propose a methodology that employs clustering to create the training sets to train different classification algorithms. We compared the performance of different classifiers along several different performance measures and consistently found that using clustering as a precursor to form the training set gives better prediction results as compared to non-clustering based training sets. We have also found the accuracies to be consistently higher than some reported in the current literature for predicting individual patient LOS. The classification techniques used in this study are interpretable, enabling us to examine the details of the classification rules learned from the data. As a result, this study provides insight into the underlying factors that influence hospital length of stay. We also examine our results with domain expert insights.
Training, Hospitals, Predictive models, Accuracy, Diseases, Prediction algorithms, Clustering algorithms, Classification, Length of Stay, Predictive Models
Ali Azari, Vandana P. Janeja, Alex Mohseni, "Predicting Hospital Length of Stay (PHLOS): A Multi-tiered Data Mining Approach", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 17-24, doi:10.1109/ICDMW.2012.69