Deep Learning and Explainable Artificial Intelligence to Predict Patients’ Choice of Hospital Levels in Urban and Rural Areas.

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Abstract

Maldistribution of healthcare resources among urban and rural areas is a significant challenge worldwide. People living in rural areas may have limited access to medical resources, and often neglect their health problems or receive insufficient care services. This research uses a deep learning approach to predict patient choices regarding hospital levels (primary, secondary or tertiary hospitals) and interpret the model decision using explainable artificial intelligence. We proposed an autoencoder-deep neural network framework and trained region-based models for the urban and rural areas. The models achieve an area under the receiver operating characteristics curve (AUC) of 0.94 and 0.95, and an accuracy of 0.93 and 0.92 for the urban and rural areas, respectively. This result indicates that region-based models are effective in improving the performance. The result is potentially leading to appropriate policy planning. Further interpretation can be done to investigate the explicit differentiation of the rural and urban scenarios.