Abstract
We explore the potential of applying machine learning techniques to
the management of patient ow in hospitals. For this project, we have obtained
the Weka machine learning library and three years of historical ward
occupancy data from Rockyview Hospital. We use Weka's classifier algorithms
and the Rockyview data to build a model of patient ow through each ward.
Using Weka, we then attempt to predict ward occupancy problems on any given
day using the model and the ward conditions from the previous day. This
process is repeated for all eighteen wards. Finally, we obtain rules (sets of
ward conditions that warn of an impending occupancy problem) for each ward
and present the results.
Notes
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