Scalable Hybrid Deep Models for Individual Pharmacy Cost Prediction

In this study, we introduce two innovative hybrid models designed for predicting individual pharmacy costs: the Autoencoder-Gated Recurrent Unit (Auto-GRU) and the GoogLeNet-Residual Network (GR-Net). Utilizing data from high utilizers obtained through the Medicaid rebate program, these models aim to provide accurate predictions of total pharmacy costs for individual patients. Our approach involves rigorous data preprocessing, including the removal of missing values, and the fine-tuning of hyperparameters using the Adam optimizer. We systematically evaluate and compare the performance of these hybrid models with that of four individual models, Autoencoder (AE), Gated Recurrent Unit (GRU), GoogLeNet, and Residual Network (ResNet), using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), training time, inference time and memory usage. Without correlation, Auto-GRU outperformed its individual models with an MSE value of 0.1675 versus GRU's MSE of 0.2806 and AE's MSE of 0.4086. GR-Net, without correlation, also beats the individual models with an MSE value of 0.0027 versus GoogLeNet's MSE value of 0.0106 and ResNet's MSE value of 0.0201. Similarly, with correlation, Auto-GRU outperformed its individual models with an MSE value of 0.2655 versus GRU's MSE of 0.4089 and AE's MSE of 0.4243. GR-Net, with correlation, also beats the individual models with an MSE value of 0.0097 versus GoogLeNet's MSE value of 0.0882 and ResNet's MSE value of 0.0106. Both the hybrid models outperformed the individual models in terms of MAE, MAE and RMSE both with and without correlation. Also, to interpret models prediction, we have implemented Local Interpretable Model-agnostic Explanations, an eXplainable Artificial Intelligence (XAI) technique. These findings highlight the robustness and effectiveness of the hybrid models in predicting pharmacy costs, underscoring their potential for integration into healthcare expense management systems.