Model comparison of patient volume prediction in digital health care
Abstract: Accurate predictions of patient volume are an essential tool to improve resource allocation and doctor utilization in the traditional, as well as the digital health care domain. Varying methods for patient volume prediction within the traditional health care domain has been studied in contemporary research, while the concept remains underexplored within the digital health care domain. In this paper, an evaluation of how two different non-linear state-of-the-art time series prediction models compare when predicting patient volume within the digital health care domain is presented. The models compared are the feed forward Multi-layer Percepron (MLP) and the recursive Long Short-Term Memory (LSTM) network. The results imply that the prediction problem itself is straightforward, while also indicating that there are significant differences in prediction accuracy between the evaluated models. The conclusions presented state that that the LSTM model offers substantial prediction advantages that outweigh the complexity overhead for the given problem.
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