Enhancing length of stay prediction by learning similarity-aware representations for hospitalized patients

This paper focuses on predicting the length of stay for patients on the first day of admission and propose a predictive model named DGLoS. In order to capture the influence of various complex factors on the length of stay as well as the dependencies among various factors, DGLoS uses a deep neural network to model both the patient information and diagnostic information. Targeting at different attribution types, we utilize different coding methods to convert raw data to the input features. Besides, we find that similar patients have closer lengths of stay. Therefore, we further design a module based on graph representation learning to generate patients’ similarity-aware representations, capturing the similarity between patients and therefore enhancing predictions. These similarity-aware representations are incorporated into the output of the deep neural network to jointly perform the prediction. We have conducted comprehensive experiments on a real-world hospitalization dataset. The performance comparison shows that our proposed DGLoS model improves predictive performance and the significance test demonstrates the improvement is significant. The ablation study verifies the effectiveness of each of the proposed components and the hyper-parameter investigation shows the robustness of the proposed model.