HealthGAT: Node Classifications in Electronic Health Records using Graph
  Attention Networks
Fahmida Liza Piya Mehak Gupta Rahmatollah Beheshti
While electronic health records (EHRs) are widely used across various
applications in healthcare, most applications use the EHRs in their raw
(tabular) format. Relying on raw or simple data pre-processing can greatly
limit the performance or even applicability of downstream tasks using EHRs. To
address this challenge, we present HealthGAT, a novel graph attention network
framework that utilizes a hierarchical approach to generate embeddings from
EHR, surpassing traditional graph-based methods. Our model iteratively refines
the embeddings for medical codes, resulting in improved EHR data analysis. We
also introduce customized EHR-centric auxiliary pre-training tasks to leverage
the rich medical knowledge embedded within the data. This approach provides a
comprehensive analysis of complex medical relationships and offers significant
advancement over standard data representation techniques. HealthGAT has
demonstrated its effectiveness in various healthcare scenarios through
comprehensive evaluations against established methodologies. Specifically, our
model shows outstanding performance in node classification and downstream tasks
such as predicting readmissions and diagnosis classifications.
  Our code is available at https://github.com/healthylaife/HealthGAT