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

TL;DR

This study develops a graph-based deep learning model using Diffusion GraphSAGE to predict hospital transfer risk for COVID-19 patients in medicalized hotels, achieving high accuracy and interpretability to aid early intervention.

Contribution

It introduces a novel graph neural network approach tailored for risk prediction in a community healthcare setting, outperforming traditional methods.

Findings

Achieved AUC scores above 0.83 for risk prediction.

Identified a high-risk patient cluster with distinct clinical features.

Demonstrated the model's potential for early detection of deterioration.

Abstract

The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to develop and evaluate a graph-based deep learning approach for progressive hospital transfer risk prediction in a medicalized hotel setting. Vital sign measurements were obtained for 632 patients and daily patient similarity graphs were constructed. Inductive graph convolutional network models were trained on top of the temporally integrated graphs to predict hospital transfer risk. The proposed models achieved AUC scores above 0.83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods. A post-hoc analysis on the constructed diffusion-based graph using Local Clustering Coefficient discovered a high-risk cluster with significantly older mean age, higher body temperature, lower SpO2, and shorter length of stay. Further time-to-hospital-transfer survival analysis also revealed a significant decrease in survival probability in the discovered high-risk cluster. The obtained results demonstrated promising predictability and interpretability of the proposed graph-based approach. This technique may help preemptively detect high-risk patients at community-based medical facilities similar to a medicalized hotel.