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.

翻译：临床证据涵盖患者、干预措施（如药物或物理治疗）、问题及结果之间的关联与影响。临床证据推荐的目的是为医疗从业者提供相关信息以支持其决策过程，并生成新证据。本研究的特定任务聚焦于基于临床问题的证据推荐。然而，部分临床问题与相关证据之间的直接联系往往稀疏，从而造成链接稀疏性挑战。此外，为推荐合适的证据，必须联合挖掘证据间的拓扑关系及其文本描述信息。为应对这些挑战，我们定义了两个知识图谱：证据共指图与证据文本图，分别表示证据元素间的拓扑关系与语言关系。同时引入多通道异质学习模型及融合注意力机制，以处理证据推荐中的共指-文本异质性。实验表明，我们的模型在公开数据集上优于现有最先进方法。