Healthcare Data Mining: Predicting Hospital Length of Stay PHLOS

Authors:
Ali Azari;Vandana P. Janeja;Alex Mohseni
Affiliations:
Information System department, University of Maryland, Baltimore County UMBC, Baltimore, MD, USA;Information System department, University of Maryland, Baltimore County UMBC, Baltimore, MD, USA;Emergency Medicine Associates, Potomac, MD, USA
Venue:
International Journal of Knowledge Discovery in Bioinformatics
Year:
2012

Citing 8
Cited 0

Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
Random Forests

Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

Expert Systems with Applications: An International Journal
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

A model to predict the Length of Stay LOS for hospitalized patients can be an effective tool for measuring the consumption of hospital resources. 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, the authors propose an approach for Predicting Hospital Length of Stay PHLOS using a multi-tiered data mining approach. In their aproach, the authors form training sets, using groups of similar claims identified by k-means clustering and perfom classification using ten different classifiers. The authors provide a combined measure of performance to statistically evaluate and rank the classifiers for different levels of clustering. They 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. The authors have also found the accuracies to be consistently higher than some reported in the current literature for predicting individual patient LOS. Binning the LOS to three groups of short, medium and long stays, their method identifies patients who need aggressive or moderate early interventions to prevent prolonged stays. The classification techniques used in this study are interpretable, enabling them 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. They also examine the authors' prediction results for three randomly selected conditions with domain expert insights.