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The global COVID-19 pandemic has caused more than six million deathsworldwide. Medicalized hotels were established in Taiwan as quarantinefacilities for COVID-19 patients with no or mild symptoms. Due to limitedmedical care available at these hotels, it is of paramount importance toidentify patients at risk of clinical deterioration. This study aimed todevelop and evaluate a graph-based deep learning approach for progressivehospital transfer risk prediction in a medicalized hotel setting. Vital signmeasurements were obtained for 632 patients and daily patient similarity graphswere constructed. Inductive graph convolutional network models were trained ontop of the temporally integrated graphs to predict hospital transfer risk. Theproposed models achieved AUC scores above 0.83 for hospital transfer riskprediction based on the measurements of past 1, 2, and 3 days, outperformingbaseline machine learning methods. A post-hoc analysis on the constructeddiffusion-based graph using Local Clustering Coefficient discovered a high-riskcluster with significantly older mean age, higher body temperature, lower SpO2,and shorter length of stay. Further time-to-hospital-transfer survival analysisalso revealed a significant decrease in survival probability in the discoveredhigh-risk cluster. The obtained results demonstrated promising predictabilityand interpretability of the proposed graph-based approach. This technique mayhelp preemptively detect high-risk patients at community-based medicalfacilities similar to a medicalized hotel.

Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE