Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDMW.2012.69 | ICDM Workshops |
Keywords | Field | DocType |
health care,classification rules,classification,pattern clustering,length of stay,different performance measure,learning (artificial intelligence),predictive models,pattern classification,prolonged los,different classification algorithm,predicting hospital length of stay,hospitals,classification technique,training sets,different classifier,phlos,hospitalized patients,multitiered data mining approach,classification rule,multi-tiered data mining approach,better prediction result,data mining,classifier,predicting hospital length,training set,healthcare providers,classification algorithms,clustering,learning artificial intelligence | Training set,Health care,Data mining,Psychological intervention,Subject-matter expert,Pattern clustering,Computer science,Prediction algorithms,Artificial intelligence,Statistical classification,Cluster analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-4673-5164-5 | 9 |
PageRank | References | Authors |
0.87 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Azari | 1 | 12 | 2.29 |
| Vandana P. Janeja | 2 | 141 | 18.93 |
| Alex Mohseni | 3 | 12 | 1.62 |