Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices.

Radiological outcomes following surgical fixation with wires versus moulded cast for patients with a dorsally displaced fracture of the distal radius: a radiographic analysis from the DRAFFT2 trial.

Aims: The primary aim of this study was to report the radiological outcomes of patients with a dorsally displaced distal radius fracture who were randomized to a moulded cast or surgical fixation with wires following manipulation and closed reduction of their fracture. The secondary aim was to correlate radiological outcomes with patient-reported outcome measures (PROMs) in the year following injury.

Methods: Participants were recruited as part of DRAFFT2, a UK multicentre clinical trial.

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing.

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors.

Reverse triage in COVID surge planning: a case study of an allied health supported clinical care pathway in an acute hospital setting.

Objective This case study describes the development and outcomes of a new integrated and multidisciplinary care pathway. Spearheaded by allied health, the 'COVID community navigator team', applied established principles of reverse triage to create additional surge capacity. Methods A retrospective cohort study examined workflow patterns using electronic medical records of patients who received navigator input at the Royal Melbourne Hospital between 20 September 2021 and 20 December 2021.