AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures.

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 ML

SUBMITTER: Kaushik S 

PROVIDER: S-EPMC7931939 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature