Using LSTMs for Predicting Patient's Expenditure on Medications

Abstract

Managing expenditure on medications is a serious challenge faced by patients, in particular for those who cannot afford costly health care. Predicting patient's spending on medications becomes crucial for efficient planning, budgeting, and decision-making. However, little attention has been given to predicting patient expenditure using deep time-series forecasting methods. The primary objective of this paper is the time-series forecasting of patient expenditures on medications using both traditional and deep time-series forecasting methods. A traditional Auto Regressive Integrated Moving Average (ARIMA) model; and, two deep models, a standard Long Short-Term Memory (LSTM) model and a stacked LSTM model were calibrated to predict the monthly expenditure on medication for 50,000+ patients in the US between 2011 and 2015. The first 48 months were used for training the models and the remaining 12 months were used for testing the models. Results revealed that the stacked LSTM model performed better than both the standard LSTM and ARIMA models during test conditions. Overall, both the deep time-series models performed better than the traditional time-series ARIMA model. We highlight the implications of our results for forecasting time-series data involving patient journeys.