Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation

TL;DR

This paper introduces custom2vec, a framework for customizing knowledge graph embeddings with user preferences, improving clinical trial recommendations by incorporating manual annotations and achieving better predictive performance.

Contribution

The paper presents a novel method for customizing knowledge graph embeddings using user preferences, enhancing clinical trial recommendation accuracy.

Findings

custom2vec outperforms conventional embedding methods in clinical trial prediction tasks

Incorporating user annotations improves recommendation relevance

Effective in scenarios like immuno-oncology trial suggestions

Abstract

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.