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

By Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai. AAAI Conference on Artificial Intelligence, 2019

301 citations recorded by the source corpus.

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. 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.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/58532a453553d68f6e1efd86adb843479a3d09cc

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Utilizing graph machine learning within drug discovery and development (2021, background): “Finally, at a higher level of abstraction, knowledge graphs can represent the complex relationships between drugs, side effects, diagnosis, associated treatments and test results [24, 25] as found in electronic medical records (EMR).”