  Clinical evidence encompasses the associations and impacts between patients,
interventions (such as drugs or physiotherapy), problems, and outcomes. The
goal of recommending clinical evidence is to provide medical practitioners with
relevant information to support their decision-making processes and to generate
new evidence. Our specific task focuses on recommending evidence based on
clinical problems. However, the direct connections between certain clinical
problems and related evidence are often sparse, creating a challenge of link
sparsity. Additionally, to recommend appropriate evidence, it is essential to
jointly exploit both topological relationships among evidence and textual
information describing them. To address these challenges, we define two
knowledge graphs: an Evidence Co-reference Graph and an Evidence Text Graph, to
represent the topological and linguistic relations among evidential elements,
respectively. We also introduce a multi-channel heterogeneous learning model
and a fusional attention mechanism to handle the co-reference-text
heterogeneity in evidence recommendation. Our experiments demonstrate that our
model outperforms state-of-the-art methods on open data.
