Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=kUijvcq45rs


Duration: 41:25
1,407 views
47

Speaker(s): Edward Choi
Facilitator(s): Nabila Abraham

Find the recording, slides, and more info at https://ai.science/e/gct-learning-the-graphical-structure-of-electronic-health-records-with-graph-convolutional-transformer--dDLvXsLBu83dJfLnm4uD

Motivation / Abstract
Electronic health records (EHR) are a connected data structure that can be modelled as a graphical structure. Research has shown that using graphical EHR is superior on predictive tasks than simply assuming no data connectivity. However, EHR data doesn't always contain structural information making it difficult to actually create graphical EHR. The authors propose the Graph Convolutional Transformer (GCT), a novel approach to jointly learn the hidden structure while performing various prediction tasks when the structure information is unavailable. The proposed model consistently outperformed previous
approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph
reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.

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