Forecasting healthcare expenditure using ConvLSTM
Date
2023-05
Authors
Lawande, Saurabh Nitin
Major Professor
Quinn, Christopher
Advisor
Committee Member
Bao, Forrest Sheng
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Altmetrics
Abstract
There have been multiple methods proposed both neural and statistical, in literature, to help
predict healthcare expenditure. The main goal of this work was to evaluate the neural techniques
and use them to predict the average weekly expenditure by patients on medications. In this
paper, autoregressive integrated moving averages and ConvLSTM models were used in
forecasting. These models were carefully calibrated and used to predict healthcare expenditure.
From observing the results, it was noted that the ConvLSTM clearly outperformed the ARIMA
model in predicting the outcome for both the medications
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Type
creative component
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Copyright
2023