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Systematic reviews (SRs) are a crucial component of evidence-based clinical practice. Unfortunately, SRs are labor-intensive and unscalable with the exponential growth in literature. Automating evidence synthesis using machine learning models has been proposed but solely focuses on the text and ignores additional features like citation information. Recent work demonstrated that citation embeddings can outperform the text itself, suggesting that better network representation may expedite SRs. Yet, how to utilize the rich information in heterogeneous information networks (HIN) for network embeddings is understudied. Existing HIN models fail to produce a high-quality embedding compared to simply running state-of-the-art homogeneous network models. To address existing HIN model limitations, we propose SR-CoMbEr, a community-based multi-view graph convolutional network for learning better embeddings for evidence synthesis. Our model automatically discovers article communities to learn robust embeddings that simultaneously encapsulate the rich semantics in HINs. We demonstrate the effectiveness of our model to automate 15 SRs. 

With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature. 

Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.