Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation
This addresses the problem of improving clinical study recommendations for researchers by customizing embeddings, though it appears incremental as it builds on existing knowledge graph embedding methods.
The authors tackled the challenge of customizing knowledge graph embeddings for clinical trial recommendations by proposing custom2vec, a framework that incorporates user preferences through custom nodes and links. Their approach achieved better performance than conventional methods in recommending non-small cell lung cancer trials, though specific numerical results were not provided.
Inferring knowledge from clinical trials using knowledge graph embedding is an emerging area. However, customizing graph embeddings for different use cases remains a significant challenge. We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the embeddings. It captures user preferences by adding custom nodes and links derived from manually vetted results of a separate information retrieval method. We propose a joint learning objective to preserve the original network structure while incorporating the user's custom annotations. We hypothesize that the custom training improves user-expected predictions, for example, in link prediction tasks. We demonstrate the effectiveness of custom2vec for clinical trials related to non-small cell lung cancer (NSCLC) with two customization scenarios: recommending immuno-oncology trials evaluating PD-1 inhibitors and exploring similar trials that compare new therapies with a standard of care. The results show that custom2vec training achieves better performance than the conventional training methods. Our approach is a novel way to customize knowledge graph embeddings and enable more accurate recommendations and predictions.