Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

@article{Wanyan2021DeepLW,
  title={Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records},
  author={Tingyi Wanyan and Hossein Honarvar and Ariful Azad and Ying Ding and Benjamin Scott Glicksberg},
  journal={Data Intelligence},
  year={2021},
  volume={3},
  pages={329-339}
}
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM) on electronic health record (EHR) data and used the resulting embedding… 
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