[Day 119] Graph Convolutional Transformer application on electronic health records
Hello :) Today is Day 119! A quick summary of today: read Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer by Choi et al. (2020) saw: Four Ways of Thinking: Statistical, Interactive, Chaotic and Complex - David Sumpter ( youtube ) I learned about Graph Transformers on Day 110 and I had saved this paper from Professor Choi and wanted to read it for a while. Firstly, I wrote some summary notes of it . One, it involves the usage of graphs, and two - Professor Edward Choi wrote it (I studied some of his lectures on intro to AI ) Studying electronic healthcare records (EHR) using deep learning can help in various tasks, including predicting diagnosis, learning medical ceoncept representations, and making interpretable predictions. EHR data is often store as hierarchical graphs as in the picture. The common way to to process this data is to consider each encounter as an unordered set of features which does not care about its graphical str