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Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals
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DOI：10.1109/ACCESS.2023.3305379.png)
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Abstract
En 中文
In healthcare, a huge amount is paid to meet the requirements of High-Need High-Cost (HNHC) patients, also known as super-utilizers. The major aim of the proposed study is to predict HNHC patients. This paper proposes hybrid Deep Learning (DL) models that identify HNHC patients, and help the management to manage their resources and budgets accordingly. We use the strategy of the Interquartile Range (IQR) that specifies the range of HNHC. The dataset used in this work has a lot of inconsistencies that are tackled by different techniques. Using three DL models, Gated Recurrent Unit (GRU), Fully Convolutional Network (FCN) and Vanilla Recurrent Neural Network (VRNN), we proposed 2 hybrid DL models, FCN-VRNN and GRU-FCN, for the prediction of HNHC patients. The performance of these models is evaluated based on different performance metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), R-Squared (R-2) and execution time. The results depict that the proposed hybrid models show more competitive and outperforming results than the individual models. Proposed hybrid system model 1 FCN-VRNN achieves MSE of 0.4%, RMSE of 7%, MAPE of 29.8%, MAE of 3%, and R-2 of 99.4%. Proposed hybrid system model 2 GRU-FCN achieves MSE of 0.4%, RMSE of 6.5%, MAPE of 23.55%, MAE of 2.5%, and R-2 of 99.5%.
Keywords:
Medical services
Predictive models
Costs
Convolutional neural networks
Task analysis
Feature extraction
Data models
Deep learning
Patient monitoring
Mean square error methods
expenditure
FCN
GRU
high-cost
healthcare
super-utilizers
VRNN
smart hospitals
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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