Medicine data

Machine learning offers a wide range of techniques to predict medicine expenditures using historical expenditures data as well as other healthcare variables. For example, researchers have developed multi-layer perceptron (MLP), long-short term memory (LSTM), and convolutional neural networks (CNN) for predicting healthcare outcomes. However, recently proposed generative approaches (e.g., generative adversarial networks; GANs) are yet to be explored for time-series prediction of medicine-related expenditures. The primary objective of this research is to develop and test a generative adversarial network model (called “variance-based GAN or V-GAN”) that specifically minimizes the difference in variance between model and actual data during model training. For our model development, we used patient expenditure data of a popular pain medication in the US. In the V-GAN model, we used an LSTM model as a generator network and a CNN model as a discriminator network. The performance of V-GAN model was compared with other GAN variants and a baseline LSTM model. Results revealed that the V-GAN model outperformed other GAN-based prediction models and the LSTM model in correctly predicting future medicine expenditures of patients. Through this research, we highlight the utility of using GAN-based architectures involving variance minimization for predicting patient-related expenditures in the healthcare domain.