Speaker:Saurabh Nitin Lawande

Forecasting healthcare expenditure using ConvLSTM

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

Committee: Chris Quinn (major professor) and Forrest Bao

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