Abstract
Both statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average expenditures on certain pain medications. Two statistical models, persistence (baseline) and autoregressive integrated moving average (ARIMA), a multilayer perceptron (MLP) model, a long short-term memory (LSTM) model, and an ensemble model combining predictions of the ARIMA, MLP, and LSTM models were calibrated to predict the expenditures on two different pain medications. In the MLP and LSTM models, we compared the influence of shuffling of training data and dropout of certain nodes in MLPs and nodes and recurrent connections in LSTMs in layers during training. Results revealed that the ensemble model outperformed the persistence, ARIMA, MLP, and LSTM models across both pain medications. In general, not shuffling the training data and adding the dropout helped the MLP models and shuffling the training data and not adding the dropout helped the LSTM models across both medications. We highlight the implications of using statistical, neural, and ensemble methods for time-series forecasting of outcomes in the healthcare domain.
Highlights
Healthcare costs are rising, and patients need to manage their healthcare expenditures on medications (Bertsimas et al, 2008)
Motivated by the use of ensemble architectures, we evaluate the potential of a weighted ensemble model (Adhikari and Agrawal, 2014), which combines the best predictions of the individual autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM) models via a dynamic weight updation technique
The primary objective of this research was to compare the performance of existing statistical and neural (MLP and LSTM) models with a novel ensemble model for forecasting patient-related expenditures on medications
Summary
Healthcare costs are rising, and patients need to manage their healthcare expenditures on medications (Bertsimas et al, 2008). Predicting medication cost in the future could help patients better manage patient-related healthcare expenditures (Zhao et al, 2001). One needs data concerning patients’ medicine-purchase patterns. There exist significant amounts of digital healthcare data that can provide helpful insights into healthcare expenditures, and these data could bring about positive changes in healthcare policymaking (Farley et al, 2006). There exist data, accessing these data is a major challenge due to privacy concerns of patients, hospitals, insurance companies, and pharmaceutical companies. One way of overcoming this challenge is via anonymizing healthcare records and medicine information so that connections to specific individuals or entities are lost.
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